Overview
Dataset statistics
| Number of variables | 39 |
|---|---|
| Number of observations | 10831 |
| Missing cells | 162201 |
| Missing cells (%) | 38.4% |
| Duplicate rows | 0 |
| Duplicate rows (%) | 0.0% |
| Total size in memory | 77.7 MiB |
| Average record size in memory | 7.3 KiB |
Variable types
| Text | 22 |
|---|---|
| Numeric | 7 |
| Categorical | 5 |
| Boolean | 4 |
| Unsupported | 1 |
source_scopus has constant value "True" | Constant |
source_wos has constant value "True" | Constant |
cited_by is highly overall correlated with cited_by_scopus and 2 other fields | High correlation |
cited_by_scopus is highly overall correlated with cited_by and 3 other fields | High correlation |
cited_reference_count_wos is highly overall correlated with has_wos | High correlation |
document_type is highly overall correlated with document_type_wos and 2 other fields | High correlation |
document_type_wos is highly overall correlated with document_type and 2 other fields | High correlation |
has_scopus is highly overall correlated with cited_by_scopus and 2 other fields | High correlation |
has_wos is highly overall correlated with cited_reference_count_wos and 6 other fields | High correlation |
language_scopus is highly overall correlated with has_scopus and 1 other fields | High correlation |
language_wos is highly overall correlated with has_wos and 1 other fields | High correlation |
publication_type_wos is highly overall correlated with document_type and 2 other fields | High correlation |
times_cited_wos_all is highly overall correlated with cited_by and 3 other fields | High correlation |
times_cited_wos_core is highly overall correlated with cited_by and 3 other fields | High correlation |
year is highly overall correlated with year_wos | High correlation |
year_wos is highly overall correlated with has_wos and 1 other fields | High correlation |
document_type is highly imbalanced (58.5%) | Imbalance |
has_scopus is highly imbalanced (64.6%) | Imbalance |
language_scopus is highly imbalanced (93.3%) | Imbalance |
document_type_wos is highly imbalanced (61.7%) | Imbalance |
language_wos is highly imbalanced (92.9%) | Imbalance |
journal has 2020 (18.7%) missing values | Missing |
authors has 754 (7.0%) missing values | Missing |
author_keywords has 2589 (23.9%) missing values | Missing |
index_keywords has 4570 (42.2%) missing values | Missing |
abstract has 724 (6.7%) missing values | Missing |
cited_by_scopus has 724 (6.7%) missing values | Missing |
references_count_scopus has 1001 (9.2%) missing values | Missing |
publisher_scopus has 1611 (14.9%) missing values | Missing |
language_scopus has 724 (6.7%) missing values | Missing |
affiliations_scopus has 872 (8.1%) missing values | Missing |
country_scopus has 10831 (100.0%) missing values | Missing |
source_scopus has 724 (6.7%) missing values | Missing |
title_wos has 6639 (61.3%) missing values | Missing |
year_wos has 6639 (61.3%) missing values | Missing |
journal_wos has 6639 (61.3%) missing values | Missing |
document_type_wos has 6639 (61.3%) missing values | Missing |
publication_type_wos has 6639 (61.3%) missing values | Missing |
authors_wos has 6640 (61.3%) missing values | Missing |
author_full_names_wos has 6640 (61.3%) missing values | Missing |
author_keywords_wos has 6935 (64.0%) missing values | Missing |
keywords_plus_wos has 8288 (76.5%) missing values | Missing |
abstract_wos has 6720 (62.0%) missing values | Missing |
times_cited_wos_core has 6639 (61.3%) missing values | Missing |
times_cited_wos_all has 6639 (61.3%) missing values | Missing |
cited_reference_count_wos has 6639 (61.3%) missing values | Missing |
wos_categories has 6654 (61.4%) missing values | Missing |
research_areas has 6654 (61.4%) missing values | Missing |
publisher_wos has 6639 (61.3%) missing values | Missing |
language_wos has 6639 (61.3%) missing values | Missing |
affiliations_wos has 6848 (63.2%) missing values | Missing |
addresses_wos has 6648 (61.4%) missing values | Missing |
source_wos has 6639 (61.3%) missing values | Missing |
cited_by is highly skewed (γ1 = 47.31713708) | Skewed |
cited_by_scopus is highly skewed (γ1 = 45.89522471) | Skewed |
times_cited_wos_core is highly skewed (γ1 = 36.20691352) | Skewed |
times_cited_wos_all is highly skewed (γ1 = 36.78078926) | Skewed |
country_scopus is an unsupported type, check if it needs cleaning or further analysis | Unsupported |
cited_by has 2480 (22.9%) zeros | Zeros |
cited_by_scopus has 2301 (21.2%) zeros | Zeros |
times_cited_wos_core has 1047 (9.7%) zeros | Zeros |
times_cited_wos_all has 903 (8.3%) zeros | Zeros |
Reproduction
| Analysis started | 2026-01-14 04:55:37.066506 |
|---|---|
| Analysis finished | 2026-01-14 04:56:03.477134 |
| Duration | 26.41 seconds |
| Software version | ydata-profiling vv4.18.1 |
| Download configuration | config.json |
Variables
doi
Text
| Distinct | 10830 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 1 |
| Missing (%) | < 0.1% |
| Memory size | 787.8 KiB |
Length
| Max length | 66 |
|---|---|
| Median length | 58 |
| Mean length | 25.476547 |
| Min length | 12 |
Unique
| Unique | 10830 ? |
|---|---|
| Unique (%) | 100.0% |
Sample
| 1st row | 10.1002/(sici)1096-9128(199601)8:1<47::aid-cpe194>3.0.co;2-9 |
|---|---|
| 2nd row | 10.1002/(sici)1097-0193(1999)8:2/3<128::aid-hbm10>3.0.co;2-g |
| 3rd row | 10.1002/(sici)1097-0363(199706)24:12<1321::aid-fld562>3.0.co;2-l |
| 4th row | 10.1002/(sici)1097-0363(199706)24:12<1353::aid-fld564>3.0.co;2-6 |
| 5th row | 10.1002/(sici)1097-0363(199706)24:12<1371::aid-fld565>3.0.co;2-7 |
| Value | Count | Frequency (%) |
| 10.1002/9780470670606.wbecc0026 | 1 | < 0.1% |
| 10.1002/(sici)1096-9128(199601)8:1<47::aid-cpe194>3.0.co;2-9 | 1 | < 0.1% |
| 10.1002/(sici)1097-0193(1999)8:2/3<128::aid-hbm10>3.0.co;2-g | 1 | < 0.1% |
| 10.1002/(sici)1097-0363(199706)24:12<1321::aid-fld562>3.0.co;2-l | 1 | < 0.1% |
| 10.1002/(sici)1097-0363(199706)24:12<1353::aid-fld564>3.0.co;2-6 | 1 | < 0.1% |
| 10.1002/(sici)1097-0363(199706)24:12<1371::aid-fld565>3.0.co;2-7 | 1 | < 0.1% |
| 10.1002/(sici)1097-0363(199706)24:12<1417::aid-fld567>3.0.co;2-n | 1 | < 0.1% |
| 10.1002/(sici)1097-0363(199706)24:12<1449::aid-fld569>3.0.co;2-8 | 1 | < 0.1% |
| 10.1002/(sici)1098-111x(199702)12:2<105::aid-int1>3.0.co;2-u | 1 | < 0.1% |
| 10.1002/(sici)1098-111x(199911)14:11<1071::aid-int1>3.0.co;2-j | 1 | < 0.1% |
| Other values (10820) | 10820 |
Most occurring characters
| Value | Count | Frequency (%) |
| 1 | 43930 | |
| 0 | 42913 | |
| . | 24004 | 8.7% |
| 2 | 20402 | 7.4% |
| 3 | 16725 | 6.1% |
| 9 | 14171 | 5.1% |
| 7 | 12822 | 4.6% |
| 4 | 12372 | 4.5% |
| 5 | 12348 | 4.5% |
| 8 | 11762 | 4.3% |
| Other values (38) | 64462 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 275911 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 1 | 43930 | |
| 0 | 42913 | |
| . | 24004 | 8.7% |
| 2 | 20402 | 7.4% |
| 3 | 16725 | 6.1% |
| 9 | 14171 | 5.1% |
| 7 | 12822 | 4.6% |
| 4 | 12372 | 4.5% |
| 5 | 12348 | 4.5% |
| 8 | 11762 | 4.3% |
| Other values (38) | 64462 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 275911 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 1 | 43930 | |
| 0 | 42913 | |
| . | 24004 | 8.7% |
| 2 | 20402 | 7.4% |
| 3 | 16725 | 6.1% |
| 9 | 14171 | 5.1% |
| 7 | 12822 | 4.6% |
| 4 | 12372 | 4.5% |
| 5 | 12348 | 4.5% |
| 8 | 11762 | 4.3% |
| Other values (38) | 64462 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 275911 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 1 | 43930 | |
| 0 | 42913 | |
| . | 24004 | 8.7% |
| 2 | 20402 | 7.4% |
| 3 | 16725 | 6.1% |
| 9 | 14171 | 5.1% |
| 7 | 12822 | 4.6% |
| 4 | 12372 | 4.5% |
| 5 | 12348 | 4.5% |
| 8 | 11762 | 4.3% |
| Other values (38) | 64462 |
title
Text
| Distinct | 10776 |
|---|---|
| Distinct (%) | 99.5% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 1.7 MiB |
Length
| Max length | 476 |
|---|---|
| Median length | 259 |
| Mean length | 93.42009 |
| Min length | 6 |
Unique
| Unique | 10731 ? |
|---|---|
| Unique (%) | 99.1% |
Sample
| 1st row | Benchmarking the computation and communication performance of the CM-5 |
|---|---|
| 2nd row | Computational modeling of high-level cognition and brain function |
| 3rd row | Parallel computation of incompressible flows with complex geometries |
| 4th row | Parallel finite element simulation of large ram-air parachutes |
| 5th row | Parallel finite element methods for large-scale computation of storm surges and tidal flows |
| Value | Count | Frequency (%) |
| of | 5376 | 4.1% |
| and | 4854 | 3.7% |
| in | 4486 | 3.4% |
| computational | 4310 | 3.3% |
| thinking | 4264 | 3.3% |
| the | 3745 | 2.9% |
| a | 3593 | 2.8% |
| for | 2646 | 2.0% |
| to | 1967 | 1.5% |
| learning | 1743 | 1.3% |
| Other values (13021) | 93466 |
Most occurring characters
| Value | Count | Frequency (%) |
| 119386 | 11.8% | |
| i | 80198 | 7.9% |
| n | 79272 | 7.8% |
| e | 76564 | 7.6% |
| t | 67002 | 6.6% |
| a | 66775 | 6.6% |
| o | 66645 | 6.6% |
| r | 46219 | 4.6% |
| s | 41410 | 4.1% |
| l | 34944 | 3.5% |
| Other values (690) | 333418 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 1011833 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 119386 | 11.8% | |
| i | 80198 | 7.9% |
| n | 79272 | 7.8% |
| e | 76564 | 7.6% |
| t | 67002 | 6.6% |
| a | 66775 | 6.6% |
| o | 66645 | 6.6% |
| r | 46219 | 4.6% |
| s | 41410 | 4.1% |
| l | 34944 | 3.5% |
| Other values (690) | 333418 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 1011833 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 119386 | 11.8% | |
| i | 80198 | 7.9% |
| n | 79272 | 7.8% |
| e | 76564 | 7.6% |
| t | 67002 | 6.6% |
| a | 66775 | 6.6% |
| o | 66645 | 6.6% |
| r | 46219 | 4.6% |
| s | 41410 | 4.1% |
| l | 34944 | 3.5% |
| Other values (690) | 333418 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 1011833 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 119386 | 11.8% | |
| i | 80198 | 7.9% |
| n | 79272 | 7.8% |
| e | 76564 | 7.6% |
| t | 67002 | 6.6% |
| a | 66775 | 6.6% |
| o | 66645 | 6.6% |
| r | 46219 | 4.6% |
| s | 41410 | 4.1% |
| l | 34944 | 3.5% |
| Other values (690) | 333418 |
year
Real number (ℝ)
High correlation
| Distinct | 48 |
|---|---|
| Distinct (%) | 0.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 2019.465 |
| Minimum | 1970 |
|---|---|
| Maximum | 2026 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 95.3 KiB |
Quantile statistics
| Minimum | 1970 |
|---|---|
| 5-th percentile | 2008 |
| Q1 | 2018 |
| median | 2021 |
| Q3 | 2024 |
| 95-th percentile | 2025 |
| Maximum | 2026 |
| Range | 56 |
| Interquartile range (IQR) | 6 |
Descriptive statistics
| Standard deviation | 6.025465 |
|---|---|
| Coefficient of variation (CV) | 0.0029836938 |
| Kurtosis | 6.6109073 |
| Mean | 2019.465 |
| Median Absolute Deviation (MAD) | 3 |
| Skewness | -2.1895759 |
| Sum | 21872825 |
| Variance | 36.306228 |
| Monotonicity | Not monotonic |
| Value | Count | Frequency (%) |
| 2024 | 1377 | |
| 2025 | 1366 | |
| 2023 | 1188 | |
| 2022 | 1070 | |
| 2021 | 982 | |
| 2020 | 852 | |
| 2019 | 723 | 6.7% |
| 2018 | 562 | 5.2% |
| 2017 | 467 | 4.3% |
| 2016 | 302 | 2.8% |
| Other values (38) | 1942 |
| Value | Count | Frequency (%) |
| 1970 | 1 | < 0.1% |
| 1975 | 1 | < 0.1% |
| 1980 | 1 | < 0.1% |
| 1981 | 1 | < 0.1% |
| 1982 | 1 | < 0.1% |
| 1983 | 4 | |
| 1984 | 6 | |
| 1986 | 3 | |
| 1987 | 3 | |
| 1988 | 7 |
| Value | Count | Frequency (%) |
| 2026 | 64 | 0.6% |
| 2025 | 1366 | |
| 2024 | 1377 | |
| 2023 | 1188 | |
| 2022 | 1070 | |
| 2021 | 982 | |
| 2020 | 852 | |
| 2019 | 723 | |
| 2018 | 562 | |
| 2017 | 467 | 4.3% |
journal
Text
Missing
| Distinct | 2639 |
|---|---|
| Distinct (%) | 30.0% |
| Missing | 2020 |
| Missing (%) | 18.7% |
| Memory size | 856.7 KiB |
Length
| Max length | 242 |
|---|---|
| Median length | 115 |
| Mean length | 43.213256 |
| Min length | 3 |
Unique
| Unique | 1666 ? |
|---|---|
| Unique (%) | 18.9% |
Sample
| 1st row | Concurrency Practice and Experience |
|---|---|
| 2nd row | Human Brain Mapping |
| 3rd row | International Journal for Numerical Methods in Fluids |
| 4th row | International Journal for Numerical Methods in Fluids |
| 5th row | International Journal for Numerical Methods in Fluids |
| Value | Count | Frequency (%) |
| of | 2732 | 5.6% |
| and | 2708 | 5.6% |
| education | 2457 | 5.1% |
| in | 2253 | 4.6% |
| journal | 1784 | 3.7% |
| conference | 1673 | 3.4% |
| science | 1520 | 3.1% |
| international | 1488 | 3.1% |
| on | 1374 | 2.8% |
| computer | 1253 | 2.6% |
| Other values (2267) | 29387 |
Most occurring characters
| Value | Count | Frequency (%) |
| 39818 | 10.5% | |
| n | 29564 | 7.8% |
| e | 26567 | 7.0% |
| o | 21919 | 5.8% |
| i | 20804 | 5.5% |
| a | 17775 | 4.7% |
| t | 15316 | 4.0% |
| c | 14553 | 3.8% |
| E | 14486 | 3.8% |
| r | 14419 | 3.8% |
| Other values (67) | 165531 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 380752 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 39818 | 10.5% | |
| n | 29564 | 7.8% |
| e | 26567 | 7.0% |
| o | 21919 | 5.8% |
| i | 20804 | 5.5% |
| a | 17775 | 4.7% |
| t | 15316 | 4.0% |
| c | 14553 | 3.8% |
| E | 14486 | 3.8% |
| r | 14419 | 3.8% |
| Other values (67) | 165531 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 380752 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 39818 | 10.5% | |
| n | 29564 | 7.8% |
| e | 26567 | 7.0% |
| o | 21919 | 5.8% |
| i | 20804 | 5.5% |
| a | 17775 | 4.7% |
| t | 15316 | 4.0% |
| c | 14553 | 3.8% |
| E | 14486 | 3.8% |
| r | 14419 | 3.8% |
| Other values (67) | 165531 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 380752 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 39818 | 10.5% | |
| n | 29564 | 7.8% |
| e | 26567 | 7.0% |
| o | 21919 | 5.8% |
| i | 20804 | 5.5% |
| a | 17775 | 4.7% |
| t | 15316 | 4.0% |
| c | 14553 | 3.8% |
| E | 14486 | 3.8% |
| r | 14419 | 3.8% |
| Other values (67) | 165531 |
document_type
Categorical
High correlation Imbalance
| Distinct | 25 |
|---|---|
| Distinct (%) | 0.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 634.1 KiB |
| Article | |
|---|---|
| Conference paper | |
| Book chapter | |
| Review | 468 |
| Book | 188 |
| Other values (20) | 436 |
Length
| Max length | 32 |
|---|---|
| Median length | 26 |
| Mean length | 10.938233 |
| Min length | 4 |
Unique
| Unique | 5 ? |
|---|---|
| Unique (%) | < 0.1% |
Sample
| 1st row | Article |
|---|---|
| 2nd row | Conference paper |
| 3rd row | Article |
| 4th row | Article |
| 5th row | Article |
Common Values
| Value | Count | Frequency (%) |
| Article | 4874 | |
| Conference paper | 4073 | |
| Book chapter | 792 | 7.3% |
| Review | 468 | 4.3% |
| Book | 188 | 1.7% |
| Proceedings Paper | 174 | 1.6% |
| Editorial | 49 | 0.5% |
| Note | 46 | 0.4% |
| Article; Book Chapter | 46 | 0.4% |
| Erratum | 28 | 0.3% |
| Other values (15) | 93 | 0.9% |
Length
| Value | Count | Frequency (%) |
| article | 4940 | |
| paper | 4251 | |
| conference | 4073 | |
| book | 1032 | 6.4% |
| chapter | 840 | 5.2% |
| review | 477 | 3.0% |
| proceedings | 176 | 1.1% |
| editorial | 60 | 0.4% |
| note | 46 | 0.3% |
| erratum | 28 | 0.2% |
| Other values (12) | 139 | 0.9% |
Most occurring characters
| Value | Count | Frequency (%) |
| e | 23719 | |
| r | 14499 | |
| c | 10046 | |
| p | 9167 | 7.7% |
| n | 8335 | 7.0% |
| o | 6438 | 5.4% |
| t | 6023 | 5.1% |
| i | 5737 | 4.8% |
| a | 5249 | 4.4% |
| 5231 | 4.4% | |
| Other values (25) | 24028 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 118472 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| e | 23719 | |
| r | 14499 | |
| c | 10046 | |
| p | 9167 | 7.7% |
| n | 8335 | 7.0% |
| o | 6438 | 5.4% |
| t | 6023 | 5.1% |
| i | 5737 | 4.8% |
| a | 5249 | 4.4% |
| 5231 | 4.4% | |
| Other values (25) | 24028 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 118472 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| e | 23719 | |
| r | 14499 | |
| c | 10046 | |
| p | 9167 | 7.7% |
| n | 8335 | 7.0% |
| o | 6438 | 5.4% |
| t | 6023 | 5.1% |
| i | 5737 | 4.8% |
| a | 5249 | 4.4% |
| 5231 | 4.4% | |
| Other values (25) | 24028 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 118472 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| e | 23719 | |
| r | 14499 | |
| c | 10046 | |
| p | 9167 | 7.7% |
| n | 8335 | 7.0% |
| o | 6438 | 5.4% |
| t | 6023 | 5.1% |
| i | 5737 | 4.8% |
| a | 5249 | 4.4% |
| 5231 | 4.4% | |
| Other values (25) | 24028 |
cited_by
Real number (ℝ)
High correlation Skewed Zeros
| Distinct | 308 |
|---|---|
| Distinct (%) | 2.8% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 20.698366 |
| Minimum | 0 |
|---|---|
| Maximum | 10043 |
| Zeros | 2480 |
| Zeros (%) | 22.9% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 84.7 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 0 |
| Q1 | 1 |
| median | 4 |
| Q3 | 14 |
| 95-th percentile | 75 |
| Maximum | 10043 |
| Range | 10043 |
| Interquartile range (IQR) | 13 |
Descriptive statistics
| Standard deviation | 135.71259 |
|---|---|
| Coefficient of variation (CV) | 6.5566814 |
| Kurtosis | 3062.8841 |
| Mean | 20.698366 |
| Median Absolute Deviation (MAD) | 4 |
| Skewness | 47.317137 |
| Sum | 224184 |
| Variance | 18417.907 |
| Monotonicity | Not monotonic |
| Value | Count | Frequency (%) |
| 0 | 2480 | |
| 1 | 1315 | 12.1% |
| 2 | 834 | 7.7% |
| 3 | 659 | 6.1% |
| 4 | 534 | 4.9% |
| 5 | 411 | 3.8% |
| 6 | 333 | 3.1% |
| 7 | 293 | 2.7% |
| 8 | 264 | 2.4% |
| 9 | 243 | 2.2% |
| Other values (298) | 3465 |
| Value | Count | Frequency (%) |
| 0 | 2480 | |
| 1 | 1315 | |
| 2 | 834 | 7.7% |
| 3 | 659 | 6.1% |
| 4 | 534 | 4.9% |
| 5 | 411 | 3.8% |
| 6 | 333 | 3.1% |
| 7 | 293 | 2.7% |
| 8 | 264 | 2.4% |
| 9 | 243 | 2.2% |
| Value | Count | Frequency (%) |
| 10043 | 1 | |
| 5509 | 1 | |
| 3161 | 1 | |
| 3156 | 1 | |
| 2123 | 1 | |
| 1908 | 1 | |
| 1703 | 1 | |
| 1438 | 1 | |
| 1317 | 1 | |
| 1306 | 1 |
has_scopus
Boolean
High correlation Imbalance
| Distinct | 2 |
|---|---|
| Distinct (%) | < 0.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 10.7 KiB |
| True | |
|---|---|
| False | 724 |
| Value | Count | Frequency (%) |
| True | 10107 | |
| False | 724 | 6.7% |
has_wos
Boolean
High correlation
| Distinct | 2 |
|---|---|
| Distinct (%) | < 0.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 10.7 KiB |
| False | |
|---|---|
| True |
| Value | Count | Frequency (%) |
| False | 6639 | |
| True | 4192 |
authors
Text
Missing
| Distinct | 9360 |
|---|---|
| Distinct (%) | 92.9% |
| Missing | 754 |
| Missing (%) | 7.0% |
| Memory size | 1.0 MiB |
Length
| Max length | 620 |
|---|---|
| Median length | 229 |
| Mean length | 41.763124 |
| Min length | 6 |
Unique
| Unique | 8874 ? |
|---|---|
| Unique (%) | 88.1% |
Sample
| 1st row | Dinçer, K.; Bozkus, Z.; Ranka, S.; Fox, G. |
|---|---|
| 2nd row | Just, M.A.; Carpenter, P.A.; Varma, S. |
| 3rd row | Johnson, A.A.; Tezduyar, T. |
| 4th row | Kalro, V.; Aliabadi, S.; Garrard, W.; Tezduyar, T.; Mittal, S.; Stein, K. |
| 5th row | Kashiyama, K.; Saitoh, K.; Behr, M.; Tezduyar, T. |
| Value | Count | Frequency (%) |
| m | 2273 | 3.4% |
| a | 2055 | 3.1% |
| j | 1920 | 2.9% |
| s | 1881 | 2.8% |
| c | 1277 | 1.9% |
| d | 1127 | 1.7% |
| r | 1090 | 1.6% |
| y | 1024 | 1.5% |
| l | 1019 | 1.5% |
| k | 899 | 1.3% |
| Other values (16181) | 52743 |
Most occurring characters
| Value | Count | Frequency (%) |
| 57229 | 13.6% | |
| . | 43198 | 10.3% |
| , | 32894 | 7.8% |
| a | 25990 | 6.2% |
| ; | 22884 | 5.4% |
| e | 17892 | 4.3% |
| n | 16896 | 4.0% |
| i | 15251 | 3.6% |
| r | 13961 | 3.3% |
| o | 13901 | 3.3% |
| Other values (137) | 160751 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 420847 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 57229 | 13.6% | |
| . | 43198 | 10.3% |
| , | 32894 | 7.8% |
| a | 25990 | 6.2% |
| ; | 22884 | 5.4% |
| e | 17892 | 4.3% |
| n | 16896 | 4.0% |
| i | 15251 | 3.6% |
| r | 13961 | 3.3% |
| o | 13901 | 3.3% |
| Other values (137) | 160751 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 420847 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 57229 | 13.6% | |
| . | 43198 | 10.3% |
| , | 32894 | 7.8% |
| a | 25990 | 6.2% |
| ; | 22884 | 5.4% |
| e | 17892 | 4.3% |
| n | 16896 | 4.0% |
| i | 15251 | 3.6% |
| r | 13961 | 3.3% |
| o | 13901 | 3.3% |
| Other values (137) | 160751 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 420847 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 57229 | 13.6% | |
| . | 43198 | 10.3% |
| , | 32894 | 7.8% |
| a | 25990 | 6.2% |
| ; | 22884 | 5.4% |
| e | 17892 | 4.3% |
| n | 16896 | 4.0% |
| i | 15251 | 3.6% |
| r | 13961 | 3.3% |
| o | 13901 | 3.3% |
| Other values (137) | 160751 |
author_keywords
Text
Missing
| Distinct | 8181 |
|---|---|
| Distinct (%) | 99.3% |
| Missing | 2589 |
| Missing (%) | 23.9% |
| Memory size | 1.3 MiB |
Length
| Max length | 930 |
|---|---|
| Median length | 274 |
| Mean length | 98.32577 |
| Min length | 6 |
Unique
| Unique | 8123 ? |
|---|---|
| Unique (%) | 98.6% |
Sample
| 1st row | 4CAPS; Brain function; PET; T-MRI |
|---|---|
| 2nd row | Automobile; Complex geometries; Mesh generation; Parallel flow simulation |
| 3rd row | 3D flow simulations; Parachutes; Parallel computations |
| 4th row | Implicit space-time formulation; Parallel finite element method; Storm surge; Three-step explicit formulation; Tidal flow |
| 5th row | Compressible flows; Missile aerodynamics; Parallel computing methods |
| Value | Count | Frequency (%) |
| computational | 5251 | 6.3% |
| thinking | 5175 | 6.2% |
| education | 2817 | 3.4% |
| learning | 2307 | 2.8% |
| programming | 1539 | 1.8% |
| science | 1082 | 1.3% |
| design | 973 | 1.2% |
| computer | 850 | 1.0% |
| and | 717 | 0.9% |
| of | 602 | 0.7% |
| Other values (9148) | 61942 |
Most occurring characters
| Value | Count | Frequency (%) |
| 75003 | 9.3% | |
| i | 70907 | 8.7% |
| n | 63449 | 7.8% |
| e | 59468 | 7.3% |
| t | 57522 | 7.1% |
| a | 55765 | 6.9% |
| o | 50958 | 6.3% |
| r | 35586 | 4.4% |
| ; | 33956 | 4.2% |
| l | 32439 | 4.0% |
| Other values (125) | 275348 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 810401 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 75003 | 9.3% | |
| i | 70907 | 8.7% |
| n | 63449 | 7.8% |
| e | 59468 | 7.3% |
| t | 57522 | 7.1% |
| a | 55765 | 6.9% |
| o | 50958 | 6.3% |
| r | 35586 | 4.4% |
| ; | 33956 | 4.2% |
| l | 32439 | 4.0% |
| Other values (125) | 275348 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 810401 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 75003 | 9.3% | |
| i | 70907 | 8.7% |
| n | 63449 | 7.8% |
| e | 59468 | 7.3% |
| t | 57522 | 7.1% |
| a | 55765 | 6.9% |
| o | 50958 | 6.3% |
| r | 35586 | 4.4% |
| ; | 33956 | 4.2% |
| l | 32439 | 4.0% |
| Other values (125) | 275348 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 810401 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 75003 | 9.3% | |
| i | 70907 | 8.7% |
| n | 63449 | 7.8% |
| e | 59468 | 7.3% |
| t | 57522 | 7.1% |
| a | 55765 | 6.9% |
| o | 50958 | 6.3% |
| r | 35586 | 4.4% |
| ; | 33956 | 4.2% |
| l | 32439 | 4.0% |
| Other values (125) | 275348 |
index_keywords
Text
Missing
| Distinct | 6257 |
|---|---|
| Distinct (%) | 99.9% |
| Missing | 4570 |
| Missing (%) | 42.2% |
| Memory size | 2.1 MiB |
Length
| Max length | 2655 |
|---|---|
| Median length | 690 |
| Mean length | 280.32982 |
| Min length | 7 |
Unique
| Unique | 6253 ? |
|---|---|
| Unique (%) | 99.9% |
Sample
| 1st row | Bandwidth; Calculations; Computational methods; Computer networks; Data communication systems; Distributed computer systems; Mathematical models; Parallel algorithms; Performance; Standards; Synchronization; Topology; Benchmarking; Communication latency; Communication start-up time; Computational processing rate; Control network; Diagnostic network; Gaussian elimination code; Global communication; Point to point communication; Vectorization; Parallel processing systems |
|---|---|
| 2nd row | brain function; cognition; computer model; computer simulation; conference paper; image processing; imaging system; nuclear magnetic resonance imaging; priority journal; Brain; Cognition; Humans; Magnetic Resonance Imaging; Models, Neurological; Neural Networks (Computer); Thinking |
| 3rd row | Aerodynamics; Automobiles; Computer simulation; Finite element method; Flow interactions; Navier Stokes equations; Parallel processing systems; Incompressible flow; Computational fluid dynamics; air flow; incompressible flow; Navier-Stokes equations; vehicles |
| 4th row | Computational fluid dynamics; Computer simulation; Drag; Finite element method; Lift; Mathematical models; Navier Stokes equations; Newtonian flow; Parallel processing systems; Three dimensional computer graphics; Canopy inflation; Ram air parachutes; Parachutes; computer simulation; finite element method; parachutes |
| 5th row | Computational fluid dynamics; Computer simulation; Finite element method; Parallel processing systems; Tides; Tidal flows; Unstructured grid formulations; Storms; computer simulation; finite element method; storms; tidal flows |
| Value | Count | Frequency (%) |
| computational | 5667 | 3.1% |
| education | 4287 | 2.4% |
| learning | 4034 | 2.2% |
| computer | 3300 | 1.8% |
| thinkings | 3120 | 1.7% |
| programming | 2822 | 1.6% |
| students | 2640 | 1.5% |
| systems | 2371 | 1.3% |
| engineering | 1985 | 1.1% |
| computing | 1970 | 1.1% |
| Other values (11047) | 148514 |
Most occurring characters
| Value | Count | Frequency (%) |
| 174449 | 9.9% | |
| e | 139615 | 8.0% |
| i | 137124 | 7.8% |
| n | 129507 | 7.4% |
| t | 117560 | 6.7% |
| a | 113948 | 6.5% |
| o | 107694 | 6.1% |
| ; | 89057 | 5.1% |
| s | 85286 | 4.9% |
| r | 84748 | 4.8% |
| Other values (105) | 576157 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 1755145 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 174449 | 9.9% | |
| e | 139615 | 8.0% |
| i | 137124 | 7.8% |
| n | 129507 | 7.4% |
| t | 117560 | 6.7% |
| a | 113948 | 6.5% |
| o | 107694 | 6.1% |
| ; | 89057 | 5.1% |
| s | 85286 | 4.9% |
| r | 84748 | 4.8% |
| Other values (105) | 576157 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 1755145 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 174449 | 9.9% | |
| e | 139615 | 8.0% |
| i | 137124 | 7.8% |
| n | 129507 | 7.4% |
| t | 117560 | 6.7% |
| a | 113948 | 6.5% |
| o | 107694 | 6.1% |
| ; | 89057 | 5.1% |
| s | 85286 | 4.9% |
| r | 84748 | 4.8% |
| Other values (105) | 576157 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 1755145 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 174449 | 9.9% | |
| e | 139615 | 8.0% |
| i | 137124 | 7.8% |
| n | 129507 | 7.4% |
| t | 117560 | 6.7% |
| a | 113948 | 6.5% |
| o | 107694 | 6.1% |
| ; | 89057 | 5.1% |
| s | 85286 | 4.9% |
| r | 84748 | 4.8% |
| Other values (105) | 576157 |
abstract
Text
Missing
| Distinct | 9997 |
|---|---|
| Distinct (%) | 98.9% |
| Missing | 724 |
| Missing (%) | 6.7% |
| Memory size | 30.7 MiB |
Length
| Max length | 13301 |
|---|---|
| Median length | 2092 |
| Mean length | 1328.3593 |
| Min length | 23 |
Unique
| Unique | 9990 ? |
|---|---|
| Unique (%) | 98.8% |
Sample
| 1st row | Thinking Machines' CM-5 machine is a distributed-memory, message-passing computer. In the paper we devise a performance benchmark for the base and vector units and the data communication networks of the CM-5 machine. We model the communication characteristics such as communication latency and bandwidths of point-to-point and global communication primitives. We show, on a simple Gaussian elimination code, that an accurate static performance estimation of parallel algorithms is possible by using those basic machine properties connected with computation, vectorization, communication and synchronization. Furthermore, we describe the embedding of meshes or hypercubes on the CM-5 fat-tree topology and illustrate the performance results of their basic communication primitives. |
|---|---|
| 2nd row | This article describes a computational modeling architecture, 4CAPS, which is consistent with key properties of cortical function and makes good contact with functional neuroimaging results. Like earlier cognitive models such as SOAR, ACT-R, 3CAPS, and EPIC, the proposed cognitive model is implemented in a computer simulation that predicts observable variables such as human response times and error patterns. In addition, the proposed 4CAPS model accounts for the functional decomposition of the cognitive system and predicts fMRI activation levels and their localization within specific cortical regions, by incorporating key properties of cortical function into the design of the modeling system. |
| 3rd row | We present our numerical methods for the solution of large-scale incompressible flow applications with complex geometries. These methods include a stabilized finite element formulation of the Navier-Stokes equations, implementation of this formulation on parallel architectures such as the Thinking Machines CM-5 and the CRAY T3D, and automatic 3D mesh generation techniques based on Delaunay-Voronoï methods for the discretization of complex domains. All three of these methods are required for the numerical simulation of most engineering applications involving fluid flow. We apply these methods to the simulation of airflow past an automobile and fluid-particle interactions. The simulation of airflow past an automobile is of very large scale with a high level of detail and yielded many interesting airflow patterns which help in understanding the aerodynamic characteristics of such vehicles. © 1997 by John Wiley & Sons, Ltd. |
| 4th row | In the near future, large ram-air parachutes are expected to provide the capability of delivering 21 ton pay loads from altitudes as high as 25,000 ft. In development and test and evaluation of these parachutes the size of the parachute needed and the deployment stages involved make high-performance computing (HPC) simulations a desirable alternative to costly airdrop tests. Although computational simulations based on realistic, 3D, time-dependent models will continue to be a major computational challenge, advanced finite element simulation techniques recently developed for this purpose and the execution of these techniques on HPC platforms are significant steps in the direction to meet this challenge. In this paper, two approaches for analysis of the inflation and gliding of ram-air parachutes are presented. In one of the approaches the point mass flight mechanics equations are solved with the time-varying drag and lift areas obtained from empirical data. This approach is limited to parachutes with similar configurations to those for which data are available. The other approach is 3D finite element computations based on the Navier-Stokes equations governing the airflow around the parachute canopy and Newton's law of motion governing the 3D dynamics of the canopy, with the forces acting on the canopy calculated from the simulated flow field. At the earlier stages of canopy inflation the parachute is modelled as an expanding box, whereas at the later stages, as it expands, the box transforms to a parafoil and glides. These finite element computations are carried out on the massively parallel supercomputers CRAY T3D and Thinking Machines CM-5, typically with millions of coupled, non-linear finite element equations solved simultaneously at every time step or pseudo-time step of the simulation. © 1997 by John Wiley & Sons, Ltd. |
| 5th row | Massively parallel finite element methods for large-scale computation of storm surges and tidal flows are discussed here. The finite element computations, carried out using unstructured grids, are based on a three-step explicit formulation and on an implicit space-time formulation. Parallel implementations of these unstructured grid-based formulations are carried out on the Fujitsu Highly Parallel Computer AP1000 and on the Thinking Machines CM-5. Simulations of the storm surge accompanying the Ise-Bay typhoon in 1959 and of the tidal flow in Tokyo Bay serve as numerical examples. The impact of parallelization on this type of simulation is also investigated. The present methods are shown to be useful and powerful tools for the analysis of storm surges and tidal flows. © 1997 by John Wiley & Sons, Ltd. |
| Value | Count | Frequency (%) |
| the | 108742 | 5.7% |
| and | 75116 | 3.9% |
| of | 72726 | 3.8% |
| to | 49923 | 2.6% |
| in | 47752 | 2.5% |
| a | 36450 | 1.9% |
| for | 20305 | 1.1% |
| that | 19229 | 1.0% |
| is | 18454 | 1.0% |
| this | 18326 | 1.0% |
| Other values (51028) | 1456269 |
Most occurring characters
| Value | Count | Frequency (%) |
| 1912617 | ||
| e | 1280469 | 9.5% |
| t | 983849 | 7.3% |
| i | 928495 | 6.9% |
| n | 873567 | 6.5% |
| a | 853662 | 6.4% |
| o | 794139 | 5.9% |
| s | 736166 | 5.5% |
| r | 653733 | 4.9% |
| c | 463412 | 3.5% |
| Other values (416) | 3945618 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 13425727 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 1912617 | ||
| e | 1280469 | 9.5% |
| t | 983849 | 7.3% |
| i | 928495 | 6.9% |
| n | 873567 | 6.5% |
| a | 853662 | 6.4% |
| o | 794139 | 5.9% |
| s | 736166 | 5.5% |
| r | 653733 | 4.9% |
| c | 463412 | 3.5% |
| Other values (416) | 3945618 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 13425727 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 1912617 | ||
| e | 1280469 | 9.5% |
| t | 983849 | 7.3% |
| i | 928495 | 6.9% |
| n | 873567 | 6.5% |
| a | 853662 | 6.4% |
| o | 794139 | 5.9% |
| s | 736166 | 5.5% |
| r | 653733 | 4.9% |
| c | 463412 | 3.5% |
| Other values (416) | 3945618 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 13425727 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 1912617 | ||
| e | 1280469 | 9.5% |
| t | 983849 | 7.3% |
| i | 928495 | 6.9% |
| n | 873567 | 6.5% |
| a | 853662 | 6.4% |
| o | 794139 | 5.9% |
| s | 736166 | 5.5% |
| r | 653733 | 4.9% |
| c | 463412 | 3.5% |
| Other values (416) | 3945618 |
cited_by_scopus
Real number (ℝ)
High correlation Missing Skewed Zeros
| Distinct | 306 |
|---|---|
| Distinct (%) | 3.0% |
| Missing | 724 |
| Missing (%) | 6.7% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 21.363411 |
| Minimum | 0 |
|---|---|
| Maximum | 10043 |
| Zeros | 2301 |
| Zeros (%) | 21.2% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 84.7 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 0 |
| Q1 | 1 |
| median | 4 |
| Q3 | 14 |
| 95-th percentile | 77.7 |
| Maximum | 10043 |
| Range | 10043 |
| Interquartile range (IQR) | 13 |
Descriptive statistics
| Standard deviation | 140.28213 |
|---|---|
| Coefficient of variation (CV) | 6.5664665 |
| Kurtosis | 2874.1411 |
| Mean | 21.363411 |
| Median Absolute Deviation (MAD) | 4 |
| Skewness | 45.895225 |
| Sum | 215920 |
| Variance | 19679.075 |
| Monotonicity | Not monotonic |
| Value | Count | Frequency (%) |
| 0 | 2301 | |
| 1 | 1201 | 11.1% |
| 2 | 793 | 7.3% |
| 3 | 613 | 5.7% |
| 4 | 502 | 4.6% |
| 5 | 379 | 3.5% |
| 6 | 308 | 2.8% |
| 7 | 264 | 2.4% |
| 8 | 249 | 2.3% |
| 9 | 226 | 2.1% |
| Other values (296) | 3271 | |
| (Missing) | 724 | 6.7% |
| Value | Count | Frequency (%) |
| 0 | 2301 | |
| 1 | 1201 | |
| 2 | 793 | 7.3% |
| 3 | 613 | 5.7% |
| 4 | 502 | 4.6% |
| 5 | 379 | 3.5% |
| 6 | 308 | 2.8% |
| 7 | 264 | 2.4% |
| 8 | 249 | 2.3% |
| 9 | 226 | 2.1% |
| Value | Count | Frequency (%) |
| 10043 | 1 | |
| 5509 | 1 | |
| 3161 | 1 | |
| 3156 | 1 | |
| 2123 | 1 | |
| 1908 | 1 | |
| 1703 | 1 | |
| 1438 | 1 | |
| 1317 | 1 | |
| 1306 | 1 |
Missing
| Distinct | 9792 |
|---|---|
| Distinct (%) | 99.6% |
| Missing | 1001 |
| Missing (%) | 9.2% |
| Memory size | 18.5 MiB |
Length
| Max length | 2254 |
|---|---|
| Median length | 1423 |
| Mean length | 1063.7052 |
| Min length | 9 |
Unique
| Unique | 9759 ? |
|---|---|
| Unique (%) | 99.3% |
Sample
| 1st row | Solving Problems on Concurrent Processors, (1988); Hypercube Algorithms with Applications to Image Processing and Pattern Recognition, (1990); Bomans, Luc, Benchmarking the iPSC/2 hypercube multiprocessor, Concurrency Practice and Experience, 1, 1, pp. 3-18, (1989); Proceedings of the Frontiers of Massively Parallel Computation, (1992); Hockney, Roger W., Performance parameters and benchmarking of supercomputers, Parallel Computing, 17, 10-11, pp. 1111-1130, (1991); Kwan, Thomas T., Communication and computation performance of the CM-5, pp. 192-201, (1993); Leiserson, Charles E., Network architecture of the Connection Machine CM-5, pp. 272-285, (1992); Lin, Mengjou, Performance evaluation of the CM-5 interconnection network, pp. 189-198, (1993); Ponnusamy, Ravi, Experimental performance evaluation of the CM-5, Journal of Parallel and Distributed Computing, 19, 3, pp. 192-202, (1993); Bailey, David H., The nas parallel benchmarks, International Journal of High Performance Computing Applications, 5, 3, pp. 63-73, (1991) |
|---|---|
| 2nd row | Rules of the Mind, (1993); Awh, Edward, Dissociation of Storage and Rehearsal in Verbal Working Memory: Evidence from Positron Emission Tomography, Psychological Science, 7, 1, pp. 25-31, (1996); Agrammatism, (1985); Carpenter, Patricia Ann, Graded functional activation in the visuospatial system with the amount of task demand, Journal of Cognitive Neuroscience, 11, 1, pp. 9-24, (1999); Carpenter, Patricia Ann, What one intelligence test measures: A theoretical account of the processing in the Raven progressive matrices test, Psychological Review, 97, 3, pp. 404-431, (1990); Collins, Allan M., Retrieval time from semantic memory, Journal of Verbal Learning and Verbal Behavior, 8, 2, pp. 240-247, (1969); Rethinking Innateness A Connectionist Perspective on Development, (1996); Human Brain Function, (1997); Gabrieli, John D.E., The role of left prefrontal cortex in language and memory, Proceedings of the National Academy of Sciences of the United States of America, 95, 3, pp. 906-913, (1998); Grafman, Jordan Henry, Similarities and Distinctions among Current Models of Prefrontal Cortical Functions, Annals of the New York Academy of Sciences, 769, 1, pp. 337-368, (1995) |
| 3rd row | Tezduyar, Tayfun E., Computation of unsteady incompressible flows with the stabilized finite element methods: Space-time formulations, iterative strategies and massively parallel implementations, American Society of Mechanical Engineers, Pressure Vessels and Piping Division (Publication) PVP, 246, pp. 7-24, (1992); Añón, J. C R, Computation of incompressible flows with implicit finite element implementations on the Connection Machine, Computer Methods in Applied Mechanics and Engineering, 108, 1-2, pp. 99-118, (1993); Tezduyar, Tayfun E., Parallel Finite-Element Computation of 3D Flows, Computer, 26, 10, pp. 27-36, (1993); Computational Mechanics 95 Proc Int Conf on Computational Engineering Science, (1995); Pvm Parallel Virtual Machine, (1994); Añón, J. C R, An efficient communications strategy for finite element methods on the Connection Machine CM-5 system, Computer Methods in Applied Mechanics and Engineering, 113, 3-4, pp. 363-387, (1994); Mesh Generation and Update Strategies for Parallel Computation of Flow Problems with Moving Boundaries and Interfaces, (1995); Technical Report, (1995); Aliabadi, Shabrouz K., Parallel fluid dynamics computations in aerospace applications, International Journal for Numerical Methods in Fluids, 21, 10, pp. 783-805, (1995); Tezduyar, Tayfun E., Massively parallel finite element simulation of compressible and incompressible flows, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 157-177, (1994) |
| 4th row | Añón, J. C R, Development testing of large ram air inflated wings, (1993); Garrard, William L., Inflation analysis of ram air inflated gliding parachutes, pp. 186-198, (1995); Aliabadi, Shahrouz K., Parallel finite element computation of the dynamics of large ram air parachutes, pp. 278-293, (1995); Añón, J. C R, SEMI-EMPIRICAL THEORY TO PREDICT THE LOAD-TIME HISTORY OF AN INFLATING PARACHUTE., pp. 177-185, (1984); University of Minnesota Parachute Systems Technology Short Courses, (1994); AIAA Paper 91 0862, (1991); Tezduyar, Tayfun E., Parallel Finite-Element Computation of 3D Flows, Computer, 26, 10, pp. 27-36, (1993); Tezduyar, Tayfun E., Massively parallel finite element simulation of compressible and incompressible flows, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 157-177, (1994); Tezduyar, Tayfun E., A new strategy for finite element computations involving moving boundaries and interfaces-The deforming-spatial-domain/space-time procedure: I. The concept and the preliminary numerical tests, Computer Methods in Applied Mechanics and Engineering, 94, 3, pp. 339-351, (1992); Tezduyar, Tayfun E., A new strategy for finite element computations involving moving boundaries and interfaces-The deforming-spatial-domain/space-time procedure: II. Computation of free-surface flows, two-liquid flows, and flows with drifting cylinders, Computer Methods in Applied Mechanics and Engineering, 94, 3, pp. 353-371, (1992) |
| 5th row | Añón, J. C R, Tide and storm surge predictions using finite element model, Journal of Hydraulic Engineering, 118, 10, pp. 1373-1390, (1992); Añón, J. C R, Massively parallel finite element method for large scale computation of storm surge, 2, pp. 79-86, (1996); Añón, J. C R, Three‐step explicit finite element computation of shallow water flows on a massively parallel computer, International Journal for Numerical Methods in Fluids, 21, 10, pp. 885-900, (1995); Añón, J. C R, Selective lumping finite element method for shallow water flow, International Journal for Numerical Methods in Fluids, 2, 1, pp. 89-112, (1982); Tezduyar, Tayfun E., Computation of unsteady incompressible flows with the stabilized finite element methods: Space-time formulations, iterative strategies and massively parallel implementations, American Society of Mechanical Engineers, Pressure Vessels and Piping Division (Publication) PVP, 246, pp. 7-24, (1992); Hughes, Thomas J.R., A multi-dimensioal upwind scheme with no crosswind diffusion., 34 )., pp. 19-35, (1979); Tezduyar, Tayfun E., FINITE ELEMENT FORMULATIONS FOR CONVECTION DOMINATED FLOWS WITH PARTICULAR EMPHASIS ON THE COMPRESSIBLE EULER EQUATIONS., (1983); Añón, J. C R, Finite element computation of compressible flows with the SUPG formulation, American Society of Mechanical Engineers, Fluids Engineering Division (Publication) FEDSM, 123, pp. 21-27, (1991); Añón, J. C R, SUPG finite element computation of compressible flows with the entropy and conservation variables formulations, Computer Methods in Applied Mechanics and Engineering, 104, 3, pp. 397-422, (1993); Aliabadi, Shabrouz K., SUPG finite element computation of viscous compressible flows based on the conservation and entropy variables formulations, Computational Mechanics, 11, 5-6, pp. 300-312, (1993) |
| Value | Count | Frequency (%) |
| of | 54578 | 3.9% |
| and | 46033 | 3.3% |
| pp | 44781 | 3.2% |
| the | 32471 | 2.3% |
| in | 31804 | 2.3% |
| a | 21555 | 1.5% |
| education | 19887 | 1.4% |
| for | 16294 | 1.2% |
| thinking | 14857 | 1.1% |
| computational | 14605 | 1.0% |
| Other values (83956) | 1109855 |
Most occurring characters
| Value | Count | Frequency (%) |
| 1396716 | 13.4% | |
| e | 699321 | 6.7% |
| n | 680439 | 6.5% |
| i | 625728 | 6.0% |
| a | 586073 | 5.6% |
| o | 568802 | 5.4% |
| t | 499713 | 4.8% |
| r | 419513 | 4.0% |
| , | 374416 | 3.6% |
| s | 342841 | 3.3% |
| Other values (250) | 4262660 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 10456222 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 1396716 | 13.4% | |
| e | 699321 | 6.7% |
| n | 680439 | 6.5% |
| i | 625728 | 6.0% |
| a | 586073 | 5.6% |
| o | 568802 | 5.4% |
| t | 499713 | 4.8% |
| r | 419513 | 4.0% |
| , | 374416 | 3.6% |
| s | 342841 | 3.3% |
| Other values (250) | 4262660 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 10456222 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 1396716 | 13.4% | |
| e | 699321 | 6.7% |
| n | 680439 | 6.5% |
| i | 625728 | 6.0% |
| a | 586073 | 5.6% |
| o | 568802 | 5.4% |
| t | 499713 | 4.8% |
| r | 419513 | 4.0% |
| , | 374416 | 3.6% |
| s | 342841 | 3.3% |
| Other values (250) | 4262660 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 10456222 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 1396716 | 13.4% | |
| e | 699321 | 6.7% |
| n | 680439 | 6.5% |
| i | 625728 | 6.0% |
| a | 586073 | 5.6% |
| o | 568802 | 5.4% |
| t | 499713 | 4.8% |
| r | 419513 | 4.0% |
| , | 374416 | 3.6% |
| s | 342841 | 3.3% |
| Other values (250) | 4262660 |
publisher_scopus
Text
Missing
| Distinct | 880 |
|---|---|
| Distinct (%) | 9.5% |
| Missing | 1611 |
| Missing (%) | 14.9% |
| Memory size | 800.9 KiB |
Length
| Max length | 130 |
|---|---|
| Median length | 107 |
| Mean length | 34.30987 |
| Min length | 3 |
Unique
| Unique | 480 ? |
|---|---|
| Unique (%) | 5.2% |
Sample
| 1st row | John Wiley and Sons Ltd |
|---|---|
| 2nd row | John Wiley and Sons Ltd |
| 3rd row | John Wiley and Sons Ltd |
| 4th row | John Wiley and Sons Ltd |
| 5th row | John Wiley and Sons Ltd |
| Value | Count | Frequency (%) |
| and | 2674 | 6.7% |
| inc | 2138 | 5.4% |
| of | 1892 | 4.8% |
| springer | 1735 | 4.4% |
| institute | 1586 | 4.0% |
| association | 1424 | 3.6% |
| for | 1400 | 3.5% |
| engineers | 1200 | 3.0% |
| computing | 1161 | 2.9% |
| machinery | 1158 | 2.9% |
| Other values (1453) | 23334 |
Most occurring characters
| Value | Count | Frequency (%) |
| 30482 | 9.6% | |
| i | 26505 | 8.4% |
| n | 25571 | 8.1% |
| e | 24984 | 7.9% |
| r | 17878 | 5.7% |
| c | 17832 | 5.6% |
| t | 16783 | 5.3% |
| s | 16775 | 5.3% |
| a | 16284 | 5.1% |
| o | 15692 | 5.0% |
| Other values (70) | 107551 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 316337 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 30482 | 9.6% | |
| i | 26505 | 8.4% |
| n | 25571 | 8.1% |
| e | 24984 | 7.9% |
| r | 17878 | 5.7% |
| c | 17832 | 5.6% |
| t | 16783 | 5.3% |
| s | 16775 | 5.3% |
| a | 16284 | 5.1% |
| o | 15692 | 5.0% |
| Other values (70) | 107551 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 316337 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 30482 | 9.6% | |
| i | 26505 | 8.4% |
| n | 25571 | 8.1% |
| e | 24984 | 7.9% |
| r | 17878 | 5.7% |
| c | 17832 | 5.6% |
| t | 16783 | 5.3% |
| s | 16775 | 5.3% |
| a | 16284 | 5.1% |
| o | 15692 | 5.0% |
| Other values (70) | 107551 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 316337 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 30482 | 9.6% | |
| i | 26505 | 8.4% |
| n | 25571 | 8.1% |
| e | 24984 | 7.9% |
| r | 17878 | 5.7% |
| c | 17832 | 5.6% |
| t | 16783 | 5.3% |
| s | 16775 | 5.3% |
| a | 16284 | 5.1% |
| o | 15692 | 5.0% |
| Other values (70) | 107551 |
language_scopus
Categorical
High correlation Imbalance Missing
| Distinct | 16 |
|---|---|
| Distinct (%) | 0.2% |
| Missing | 724 |
| Missing (%) | 6.7% |
| Memory size | 592.6 KiB |
| English | |
|---|---|
| Spanish | 128 |
| Chinese | 83 |
| Portuguese | 45 |
| Russian | 19 |
| Other values (11) | 31 |
Length
| Max length | 10 |
|---|---|
| Median length | 7 |
| Mean length | 7.0124666 |
| Min length | 6 |
Unique
| Unique | 2 ? |
|---|---|
| Unique (%) | < 0.1% |
Sample
| 1st row | English |
|---|---|
| 2nd row | English |
| 3rd row | English |
| 4th row | English |
| 5th row | English |
Common Values
| Value | Count | Frequency (%) |
| English | 9801 | |
| Spanish | 128 | 1.2% |
| Chinese | 83 | 0.8% |
| Portuguese | 45 | 0.4% |
| Russian | 19 | 0.2% |
| German | 5 | < 0.1% |
| Polish | 4 | < 0.1% |
| Italian | 4 | < 0.1% |
| Croatian | 3 | < 0.1% |
| French | 3 | < 0.1% |
| Other values (6) | 12 | 0.1% |
| (Missing) | 724 | 6.7% |
Length
| Value | Count | Frequency (%) |
| english | 9801 | |
| spanish | 128 | 1.3% |
| chinese | 83 | 0.8% |
| portuguese | 45 | 0.4% |
| russian | 19 | 0.2% |
| german | 5 | < 0.1% |
| polish | 4 | < 0.1% |
| italian | 4 | < 0.1% |
| croatian | 3 | < 0.1% |
| french | 3 | < 0.1% |
| Other values (6) | 12 | 0.1% |
Most occurring characters
| Value | Count | Frequency (%) |
| s | 10105 | |
| n | 10054 | |
| i | 10049 | |
| h | 10021 | |
| g | 9847 | |
| l | 9809 | |
| E | 9801 | |
| e | 273 | 0.4% |
| a | 180 | 0.3% |
| p | 131 | 0.2% |
| Other values (20) | 605 | 0.9% |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 70875 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| s | 10105 | |
| n | 10054 | |
| i | 10049 | |
| h | 10021 | |
| g | 9847 | |
| l | 9809 | |
| E | 9801 | |
| e | 273 | 0.4% |
| a | 180 | 0.3% |
| p | 131 | 0.2% |
| Other values (20) | 605 | 0.9% |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 70875 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| s | 10105 | |
| n | 10054 | |
| i | 10049 | |
| h | 10021 | |
| g | 9847 | |
| l | 9809 | |
| E | 9801 | |
| e | 273 | 0.4% |
| a | 180 | 0.3% |
| p | 131 | 0.2% |
| Other values (20) | 605 | 0.9% |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 70875 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| s | 10105 | |
| n | 10054 | |
| i | 10049 | |
| h | 10021 | |
| g | 9847 | |
| l | 9809 | |
| E | 9801 | |
| e | 273 | 0.4% |
| a | 180 | 0.3% |
| p | 131 | 0.2% |
| Other values (20) | 605 | 0.9% |
affiliations_scopus
Text
Missing
| Distinct | 8257 |
|---|---|
| Distinct (%) | 82.9% |
| Missing | 872 |
| Missing (%) | 8.1% |
| Memory size | 2.5 MiB |
Length
| Max length | 2979 |
|---|---|
| Median length | 610 |
| Mean length | 155.70509 |
| Min length | 6 |
Unique
| Unique | 7396 ? |
|---|---|
| Unique (%) | 74.3% |
Sample
| 1st row | Northeast Parallel Architectures Center, Syracuse University, Syracuse, NY, United States |
|---|---|
| 2nd row | Center for Cognitive Brain Imaging, Carnegie Mellon University, Pittsburgh, PA, United States; Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, United States |
| 3rd row | Army HPC Research Center, College of Science and Engineering, Minneapolis, MN, United States |
| 4th row | Army HPC Research Center, College of Science and Engineering, Minneapolis, MN, United States; Indian Institute of Technology Kanpur, Kanpur, UP, India; U.S. Army Natick RD and E Center, Natick, MA, United States |
| 5th row | Department of Civil Engineering, Chuo University, Hachioji, Tokyo, Japan; University of Minnesota Twin Cities, Minneapolis, MN, United States |
| Value | Count | Frequency (%) |
| of | 14845 | 7.6% |
| university | 9841 | 5.0% |
| united | 6752 | 3.5% |
| states | 5816 | 3.0% |
| department | 5189 | 2.7% |
| and | 4565 | 2.3% |
| de | 2667 | 1.4% |
| education | 2514 | 1.3% |
| china | 2344 | 1.2% |
| science | 2328 | 1.2% |
| Other values (9495) | 138073 |
Most occurring characters
| Value | Count | Frequency (%) |
| 184972 | 11.9% | |
| e | 124190 | 8.0% |
| n | 114070 | 7.4% |
| i | 111090 | 7.2% |
| a | 110084 | 7.1% |
| t | 93890 | 6.1% |
| o | 81846 | 5.3% |
| , | 68646 | 4.4% |
| r | 67966 | 4.4% |
| s | 53874 | 3.5% |
| Other values (156) | 540039 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 1550667 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 184972 | 11.9% | |
| e | 124190 | 8.0% |
| n | 114070 | 7.4% |
| i | 111090 | 7.2% |
| a | 110084 | 7.1% |
| t | 93890 | 6.1% |
| o | 81846 | 5.3% |
| , | 68646 | 4.4% |
| r | 67966 | 4.4% |
| s | 53874 | 3.5% |
| Other values (156) | 540039 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 1550667 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 184972 | 11.9% | |
| e | 124190 | 8.0% |
| n | 114070 | 7.4% |
| i | 111090 | 7.2% |
| a | 110084 | 7.1% |
| t | 93890 | 6.1% |
| o | 81846 | 5.3% |
| , | 68646 | 4.4% |
| r | 67966 | 4.4% |
| s | 53874 | 3.5% |
| Other values (156) | 540039 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 1550667 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 184972 | 11.9% | |
| e | 124190 | 8.0% |
| n | 114070 | 7.4% |
| i | 111090 | 7.2% |
| a | 110084 | 7.1% |
| t | 93890 | 6.1% |
| o | 81846 | 5.3% |
| , | 68646 | 4.4% |
| r | 67966 | 4.4% |
| s | 53874 | 3.5% |
| Other values (156) | 540039 |
country_scopus
Unsupported
Missing Rejected Unsupported
| Missing | 10831 |
|---|---|
| Missing (%) | 100.0% |
| Memory size | 84.7 KiB |
source_scopus
Boolean
Constant Missing
| Distinct | 1 |
|---|---|
| Distinct (%) | < 0.1% |
| Missing | 724 |
| Missing (%) | 6.7% |
| Memory size | 378.1 KiB |
| True | |
|---|---|
| (Missing) | 724 |
| Value | Count | Frequency (%) |
| True | 10107 | |
| (Missing) | 724 | 6.7% |
title_wos
Text
Missing
| Distinct | 4186 |
|---|---|
| Distinct (%) | 99.9% |
| Missing | 6639 |
| Missing (%) | 61.3% |
| Memory size | 817.6 KiB |
Length
| Max length | 242 |
|---|---|
| Median length | 168 |
| Mean length | 99.683445 |
| Min length | 6 |
Unique
| Unique | 4180 ? |
|---|---|
| Unique (%) | 99.7% |
Sample
| 1st row | The impact of inducing troubleshooting strategies via visual aids on performance in a computerized digital network task |
|---|---|
| 2nd row | Insights into computational thinking from think-aloud interviews: A systematic review |
| 3rd row | Student responses to creative coding in biomedical science education |
| 4th row | Modeling and Simulation Practices for a Computational Thinking-Enabled Engineering Workforce |
| 5th row | Modeling and simulation practices in engineering education |
| Value | Count | Frequency (%) |
| computational | 2299 | 4.3% |
| thinking | 2262 | 4.2% |
| of | 2135 | 4.0% |
| and | 2096 | 3.9% |
| in | 2036 | 3.8% |
| a | 1626 | 3.0% |
| the | 1444 | 2.7% |
| for | 1063 | 2.0% |
| learning | 927 | 1.7% |
| to | 846 | 1.6% |
| Other values (5090) | 36599 |
Most occurring characters
| Value | Count | Frequency (%) |
| 49141 | 11.8% | |
| n | 33500 | 8.0% |
| i | 33140 | 7.9% |
| e | 30281 | 7.2% |
| t | 27565 | 6.6% |
| o | 27402 | 6.6% |
| a | 26956 | 6.5% |
| r | 18877 | 4.5% |
| s | 15907 | 3.8% |
| l | 13987 | 3.3% |
| Other values (81) | 141117 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 417873 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 49141 | 11.8% | |
| n | 33500 | 8.0% |
| i | 33140 | 7.9% |
| e | 30281 | 7.2% |
| t | 27565 | 6.6% |
| o | 27402 | 6.6% |
| a | 26956 | 6.5% |
| r | 18877 | 4.5% |
| s | 15907 | 3.8% |
| l | 13987 | 3.3% |
| Other values (81) | 141117 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 417873 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 49141 | 11.8% | |
| n | 33500 | 8.0% |
| i | 33140 | 7.9% |
| e | 30281 | 7.2% |
| t | 27565 | 6.6% |
| o | 27402 | 6.6% |
| a | 26956 | 6.5% |
| r | 18877 | 4.5% |
| s | 15907 | 3.8% |
| l | 13987 | 3.3% |
| Other values (81) | 141117 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 417873 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 49141 | 11.8% | |
| n | 33500 | 8.0% |
| i | 33140 | 7.9% |
| e | 30281 | 7.2% |
| t | 27565 | 6.6% |
| o | 27402 | 6.6% |
| a | 26956 | 6.5% |
| r | 18877 | 4.5% |
| s | 15907 | 3.8% |
| l | 13987 | 3.3% |
| Other values (81) | 141117 |
year_wos
Real number (ℝ)
High correlation Missing
| Distinct | 23 |
|---|---|
| Distinct (%) | 0.5% |
| Missing | 6639 |
| Missing (%) | 61.3% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 2021.584 |
| Minimum | 1994 |
|---|---|
| Maximum | 2026 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 84.7 KiB |
Quantile statistics
| Minimum | 1994 |
|---|---|
| 5-th percentile | 2017 |
| Q1 | 2020 |
| median | 2022 |
| Q3 | 2024 |
| 95-th percentile | 2025 |
| Maximum | 2026 |
| Range | 32 |
| Interquartile range (IQR) | 4 |
Descriptive statistics
| Standard deviation | 2.9696003 |
|---|---|
| Coefficient of variation (CV) | 0.0014689473 |
| Kurtosis | 4.6535253 |
| Mean | 2021.584 |
| Median Absolute Deviation (MAD) | 2 |
| Skewness | -1.3642788 |
| Sum | 8474480 |
| Variance | 8.8185261 |
| Monotonicity | Not monotonic |
| Value | Count | Frequency (%) |
| 2025 | 669 | 6.2% |
| 2024 | 609 | 5.6% |
| 2023 | 539 | 5.0% |
| 2021 | 523 | 4.8% |
| 2022 | 522 | 4.8% |
| 2020 | 398 | 3.7% |
| 2019 | 322 | 3.0% |
| 2018 | 199 | 1.8% |
| 2017 | 173 | 1.6% |
| 2016 | 59 | 0.5% |
| Other values (13) | 179 | 1.7% |
| (Missing) | 6639 |
| Value | Count | Frequency (%) |
| 1994 | 1 | < 0.1% |
| 1995 | 1 | < 0.1% |
| 2006 | 1 | < 0.1% |
| 2007 | 2 | < 0.1% |
| 2008 | 3 | < 0.1% |
| 2009 | 11 | |
| 2010 | 5 | < 0.1% |
| 2011 | 7 | 0.1% |
| 2012 | 10 | |
| 2013 | 19 |
| Value | Count | Frequency (%) |
| 2026 | 32 | 0.3% |
| 2025 | 669 | |
| 2024 | 609 | |
| 2023 | 539 | |
| 2022 | 522 | |
| 2021 | 523 | |
| 2020 | 398 | |
| 2019 | 322 | |
| 2018 | 199 | 1.8% |
| 2017 | 173 | 1.6% |
journal_wos
Text
Missing
| Distinct | 1234 |
|---|---|
| Distinct (%) | 29.4% |
| Missing | 6639 |
| Missing (%) | 61.3% |
| Memory size | 623.1 KiB |
Length
| Max length | 242 |
|---|---|
| Median length | 116 |
| Mean length | 52.488311 |
| Min length | 2 |
Unique
| Unique | 747 ? |
|---|---|
| Unique (%) | 17.8% |
Sample
| 1st row | APPLIED COGNITIVE PSYCHOLOGY |
|---|---|
| 2nd row | APPLIED COGNITIVE PSYCHOLOGY |
| 3rd row | BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION |
| 4th row | COMPUTER APPLICATIONS IN ENGINEERING EDUCATION |
| 5th row | COMPUTER APPLICATIONS IN ENGINEERING EDUCATION |
| Value | Count | Frequency (%) |
| education | 1955 | 6.8% |
| of | 1574 | 5.4% |
| and | 1390 | 4.8% |
| on | 1096 | 3.8% |
| in | 989 | 3.4% |
| conference | 882 | 3.1% |
| the | 812 | 2.8% |
| proceedings | 756 | 2.6% |
| journal | 715 | 2.5% |
| science | 657 | 2.3% |
| Other values (1513) | 18077 |
Most occurring characters
| Value | Count | Frequency (%) |
| 24711 | ||
| E | 21982 | 10.0% |
| N | 20803 | 9.5% |
| I | 17119 | 7.8% |
| O | 16540 | 7.5% |
| C | 14756 | 6.7% |
| A | 13545 | 6.2% |
| T | 13110 | 6.0% |
| R | 9652 | 4.4% |
| S | 8667 | 3.9% |
| Other values (61) | 59146 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 220031 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 24711 | ||
| E | 21982 | 10.0% |
| N | 20803 | 9.5% |
| I | 17119 | 7.8% |
| O | 16540 | 7.5% |
| C | 14756 | 6.7% |
| A | 13545 | 6.2% |
| T | 13110 | 6.0% |
| R | 9652 | 4.4% |
| S | 8667 | 3.9% |
| Other values (61) | 59146 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 220031 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 24711 | ||
| E | 21982 | 10.0% |
| N | 20803 | 9.5% |
| I | 17119 | 7.8% |
| O | 16540 | 7.5% |
| C | 14756 | 6.7% |
| A | 13545 | 6.2% |
| T | 13110 | 6.0% |
| R | 9652 | 4.4% |
| S | 8667 | 3.9% |
| Other values (61) | 59146 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 220031 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 24711 | ||
| E | 21982 | 10.0% |
| N | 20803 | 9.5% |
| I | 17119 | 7.8% |
| O | 16540 | 7.5% |
| C | 14756 | 6.7% |
| A | 13545 | 6.2% |
| T | 13110 | 6.0% |
| R | 9652 | 4.4% |
| S | 8667 | 3.9% |
| Other values (61) | 59146 |
document_type_wos
Categorical
High correlation Imbalance Missing
| Distinct | 19 |
|---|---|
| Distinct (%) | 0.5% |
| Missing | 6639 |
| Missing (%) | 61.3% |
| Memory size | 610.9 KiB |
| Article | |
|---|---|
| Proceedings Paper | |
| Review | 190 |
| Article; Early Access | 95 |
| Article; Book Chapter | 59 |
| Other values (14) | 97 |
Length
| Max length | 32 |
|---|---|
| Median length | 7 |
| Mean length | 11.496899 |
| Min length | 4 |
Unique
| Unique | 6 ? |
|---|---|
| Unique (%) | 0.1% |
Sample
| 1st row | Article |
|---|---|
| 2nd row | Review |
| 3rd row | Article |
| 4th row | Article |
| 5th row | Article |
Common Values
| Value | Count | Frequency (%) |
| Article | 2160 | 19.9% |
| Proceedings Paper | 1591 | 14.7% |
| Review | 190 | 1.8% |
| Article; Early Access | 95 | 0.9% |
| Article; Book Chapter | 59 | 0.5% |
| Editorial Material | 40 | 0.4% |
| Correction | 13 | 0.1% |
| Meeting Abstract | 12 | 0.1% |
| Article; Proceedings Paper | 11 | 0.1% |
| Review; Early Access | 6 | 0.1% |
| Other values (9) | 15 | 0.1% |
| (Missing) | 6639 |
Length
| Value | Count | Frequency (%) |
| article | 2326 | |
| proceedings | 1602 | |
| paper | 1602 | |
| review | 202 | 3.3% |
| early | 102 | 1.6% |
| access | 102 | 1.6% |
| book | 67 | 1.1% |
| chapter | 61 | 1.0% |
| editorial | 42 | 0.7% |
| material | 42 | 0.7% |
| Other values (8) | 44 | 0.7% |
Most occurring characters
| Value | Count | Frequency (%) |
| e | 7788 | |
| r | 5821 | |
| i | 4284 | 8.9% |
| c | 4159 | 8.6% |
| P | 3205 | 6.7% |
| t | 2531 | 5.3% |
| l | 2513 | 5.2% |
| A | 2440 | 5.1% |
| 2000 | 4.1% | |
| a | 1906 | 4.0% |
| Other values (22) | 11548 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 48195 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| e | 7788 | |
| r | 5821 | |
| i | 4284 | 8.9% |
| c | 4159 | 8.6% |
| P | 3205 | 6.7% |
| t | 2531 | 5.3% |
| l | 2513 | 5.2% |
| A | 2440 | 5.1% |
| 2000 | 4.1% | |
| a | 1906 | 4.0% |
| Other values (22) | 11548 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 48195 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| e | 7788 | |
| r | 5821 | |
| i | 4284 | 8.9% |
| c | 4159 | 8.6% |
| P | 3205 | 6.7% |
| t | 2531 | 5.3% |
| l | 2513 | 5.2% |
| A | 2440 | 5.1% |
| 2000 | 4.1% | |
| a | 1906 | 4.0% |
| Other values (22) | 11548 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 48195 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| e | 7788 | |
| r | 5821 | |
| i | 4284 | 8.9% |
| c | 4159 | 8.6% |
| P | 3205 | 6.7% |
| t | 2531 | 5.3% |
| l | 2513 | 5.2% |
| A | 2440 | 5.1% |
| 2000 | 4.1% | |
| a | 1906 | 4.0% |
| Other values (22) | 11548 |
publication_type_wos
Categorical
High correlation Missing
| Distinct | 4 |
|---|---|
| Distinct (%) | 0.1% |
| Missing | 6639 |
| Missing (%) | 61.3% |
| Memory size | 567.9 KiB |
| J | |
|---|---|
| C | |
| B | 52 |
| S | 23 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | J |
|---|---|
| 2nd row | J |
| 3rd row | J |
| 4th row | J |
| 5th row | J |
Common Values
| Value | Count | Frequency (%) |
| J | 2526 | 23.3% |
| C | 1591 | 14.7% |
| B | 52 | 0.5% |
| S | 23 | 0.2% |
| (Missing) | 6639 |
Length
Common Values (Plot)
| Value | Count | Frequency (%) |
| j | 2526 | |
| c | 1591 | |
| b | 52 | 1.2% |
| s | 23 | 0.5% |
Most occurring characters
| Value | Count | Frequency (%) |
| J | 2526 | |
| C | 1591 | |
| B | 52 | 1.2% |
| S | 23 | 0.5% |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 4192 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| J | 2526 | |
| C | 1591 | |
| B | 52 | 1.2% |
| S | 23 | 0.5% |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 4192 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| J | 2526 | |
| C | 1591 | |
| B | 52 | 1.2% |
| S | 23 | 0.5% |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 4192 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| J | 2526 | |
| C | 1591 | |
| B | 52 | 1.2% |
| S | 23 | 0.5% |
authors_wos
Text
Missing
| Distinct | 3880 |
|---|---|
| Distinct (%) | 92.6% |
| Missing | 6640 |
| Missing (%) | 61.3% |
| Memory size | 615.1 KiB |
Length
| Max length | 396 |
|---|---|
| Median length | 140 |
| Mean length | 39.709139 |
| Min length | 5 |
Unique
| Unique | 3663 ? |
|---|---|
| Unique (%) | 87.4% |
Sample
| 1st row | Bordewieck, M; Elson, M |
|---|---|
| 2nd row | Pan, ZX; Cui, Y; Leighton, JP; Cutumisu, M |
| 3rd row | Gough, P; Bown, O; Campbell, CR; Poronnik, P; Ross, PM |
| 4th row | Magana, AJ; Coutinho, GS |
| 5th row | Magana, AJ; de Jong, T |
| Value | Count | Frequency (%) |
| m | 1011 | 3.5% |
| a | 940 | 3.2% |
| j | 709 | 2.4% |
| s | 692 | 2.4% |
| c | 608 | 2.1% |
| d | 505 | 1.7% |
| e | 435 | 1.5% |
| r | 424 | 1.4% |
| k | 407 | 1.4% |
| l | 388 | 1.3% |
| Other values (7385) | 23147 |
Most occurring characters
| Value | Count | Frequency (%) |
| 25075 | 15.1% | |
| , | 14498 | 8.7% |
| a | 10763 | 6.5% |
| ; | 10335 | 6.2% |
| e | 8379 | 5.0% |
| n | 7264 | 4.4% |
| i | 6546 | 3.9% |
| o | 6328 | 3.8% |
| r | 6247 | 3.8% |
| l | 4301 | 2.6% |
| Other values (67) | 66685 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 166421 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 25075 | 15.1% | |
| , | 14498 | 8.7% |
| a | 10763 | 6.5% |
| ; | 10335 | 6.2% |
| e | 8379 | 5.0% |
| n | 7264 | 4.4% |
| i | 6546 | 3.9% |
| o | 6328 | 3.8% |
| r | 6247 | 3.8% |
| l | 4301 | 2.6% |
| Other values (67) | 66685 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 166421 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 25075 | 15.1% | |
| , | 14498 | 8.7% |
| a | 10763 | 6.5% |
| ; | 10335 | 6.2% |
| e | 8379 | 5.0% |
| n | 7264 | 4.4% |
| i | 6546 | 3.9% |
| o | 6328 | 3.8% |
| r | 6247 | 3.8% |
| l | 4301 | 2.6% |
| Other values (67) | 66685 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 166421 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 25075 | 15.1% | |
| , | 14498 | 8.7% |
| a | 10763 | 6.5% |
| ; | 10335 | 6.2% |
| e | 8379 | 5.0% |
| n | 7264 | 4.4% |
| i | 6546 | 3.9% |
| o | 6328 | 3.8% |
| r | 6247 | 3.8% |
| l | 4301 | 2.6% |
| Other values (67) | 66685 |
Missing
| Distinct | 3897 |
|---|---|
| Distinct (%) | 93.0% |
| Missing | 6640 |
| Missing (%) | 61.3% |
| Memory size | 656.3 KiB |
Length
| Max length | 568 |
|---|---|
| Median length | 179 |
| Mean length | 60.631114 |
| Min length | 8 |
Unique
| Unique | 3693 ? |
|---|---|
| Unique (%) | 88.1% |
Sample
| 1st row | Bordewieck, Martin; Elson, Malte |
|---|---|
| 2nd row | Pan, Zexuan; Cui, Ying; Leighton, Jacqueline P.; Cutumisu, Maria |
| 3rd row | Gough, Phillip; Bown, Oliver; Campbell, Craig R.; Poronnik, Philip; Ross, Pauline M. |
| 4th row | Magana, Alejandra J.; Coutinho, Genisson Silva |
| 5th row | Magana, Alejandra J.; de Jong, Ton |
| Value | Count | Frequency (%) |
| m | 229 | 0.7% |
| a | 208 | 0.6% |
| wang | 161 | 0.5% |
| j | 153 | 0.5% |
| chen | 149 | 0.4% |
| maria | 144 | 0.4% |
| c | 133 | 0.4% |
| li | 132 | 0.4% |
| jose | 129 | 0.4% |
| zhang | 120 | 0.4% |
| Other values (11560) | 31763 |
Most occurring characters
| Value | Count | Frequency (%) |
| 29130 | 11.5% | |
| a | 24247 | 9.5% |
| e | 16655 | 6.6% |
| i | 16516 | 6.5% |
| n | 16447 | 6.5% |
| , | 14508 | 5.7% |
| r | 12137 | 4.8% |
| o | 11864 | 4.7% |
| ; | 10335 | 4.1% |
| l | 8358 | 3.3% |
| Other values (50) | 93908 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 254105 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 29130 | 11.5% | |
| a | 24247 | 9.5% |
| e | 16655 | 6.6% |
| i | 16516 | 6.5% |
| n | 16447 | 6.5% |
| , | 14508 | 5.7% |
| r | 12137 | 4.8% |
| o | 11864 | 4.7% |
| ; | 10335 | 4.1% |
| l | 8358 | 3.3% |
| Other values (50) | 93908 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 254105 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 29130 | 11.5% | |
| a | 24247 | 9.5% |
| e | 16655 | 6.6% |
| i | 16516 | 6.5% |
| n | 16447 | 6.5% |
| , | 14508 | 5.7% |
| r | 12137 | 4.8% |
| o | 11864 | 4.7% |
| ; | 10335 | 4.1% |
| l | 8358 | 3.3% |
| Other values (50) | 93908 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 254105 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 29130 | 11.5% | |
| a | 24247 | 9.5% |
| e | 16655 | 6.6% |
| i | 16516 | 6.5% |
| n | 16447 | 6.5% |
| , | 14508 | 5.7% |
| r | 12137 | 4.8% |
| o | 11864 | 4.7% |
| ; | 10335 | 4.1% |
| l | 8358 | 3.3% |
| Other values (50) | 93908 |
author_keywords_wos
Text
Missing
| Distinct | 3871 |
|---|---|
| Distinct (%) | 99.4% |
| Missing | 6935 |
| Missing (%) | 64.0% |
| Memory size | 780.2 KiB |
Length
| Max length | 871 |
|---|---|
| Median length | 215 |
| Mean length | 98.48922 |
| Min length | 3 |
Unique
| Unique | 3849 ? |
|---|---|
| Unique (%) | 98.8% |
Sample
| 1st row | computational thinking; networks; problem solving; troubleshooting; visual aids |
|---|---|
| 2nd row | cognitive processes; computational thinking; think-aloud |
| 3rd row | Arduino; biomedical science education; creative code; data visualization; processing |
| 4th row | computational thinking; engineering education; modeling and simulation; science and engineering workforce |
| 5th row | graduate; K-12; modeling; post-graduate; simulation |
| Value | Count | Frequency (%) |
| thinking | 3068 | 7.9% |
| computational | 2983 | 7.7% |
| education | 1992 | 5.1% |
| learning | 1358 | 3.5% |
| programming | 1169 | 3.0% |
| science | 654 | 1.7% |
| computer | 549 | 1.4% |
| educational | 421 | 1.1% |
| design | 347 | 0.9% |
| robotics | 345 | 0.9% |
| Other values (3648) | 25895 |
Most occurring characters
| Value | Count | Frequency (%) |
| 34885 | 9.1% | |
| i | 33753 | 8.8% |
| n | 30795 | 8.0% |
| t | 27399 | 7.1% |
| e | 27146 | 7.1% |
| a | 26835 | 7.0% |
| o | 24543 | 6.4% |
| r | 16779 | 4.4% |
| c | 16048 | 4.2% |
| ; | 15990 | 4.2% |
| Other values (68) | 129541 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 383714 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 34885 | 9.1% | |
| i | 33753 | 8.8% |
| n | 30795 | 8.0% |
| t | 27399 | 7.1% |
| e | 27146 | 7.1% |
| a | 26835 | 7.0% |
| o | 24543 | 6.4% |
| r | 16779 | 4.4% |
| c | 16048 | 4.2% |
| ; | 15990 | 4.2% |
| Other values (68) | 129541 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 383714 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 34885 | 9.1% | |
| i | 33753 | 8.8% |
| n | 30795 | 8.0% |
| t | 27399 | 7.1% |
| e | 27146 | 7.1% |
| a | 26835 | 7.0% |
| o | 24543 | 6.4% |
| r | 16779 | 4.4% |
| c | 16048 | 4.2% |
| ; | 15990 | 4.2% |
| Other values (68) | 129541 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 383714 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 34885 | 9.1% | |
| i | 33753 | 8.8% |
| n | 30795 | 8.0% |
| t | 27399 | 7.1% |
| e | 27146 | 7.1% |
| a | 26835 | 7.0% |
| o | 24543 | 6.4% |
| r | 16779 | 4.4% |
| c | 16048 | 4.2% |
| ; | 15990 | 4.2% |
| Other values (68) | 129541 |
keywords_plus_wos
Text
Missing
| Distinct | 2018 |
|---|---|
| Distinct (%) | 79.4% |
| Missing | 8288 |
| Missing (%) | 76.5% |
| Memory size | 499.5 KiB |
Length
| Max length | 187 |
|---|---|
| Median length | 142 |
| Mean length | 47.801809 |
| Min length | 2 |
Unique
| Unique | 1900 ? |
|---|---|
| Unique (%) | 74.7% |
Sample
| 1st row | VERBAL REPORTS; SKILLS; GAME; EDUCATION; TEACHERS; LEARN |
|---|---|
| 2nd row | COMPUTATIONAL THINKING; NARRATIVES; DESIGN; SKILLS; LEARN |
| 3rd row | CURRICULUM; STUDENTS; SKILLS; MATHEMATICS; MOTIVATION; STANDARDS; SCIENCE |
| 4th row | COMPUTATIONAL THINKING; SCIENCE; SYSTEMS; DESIGN; TOOLS; K-12 |
| 5th row | COMPUTATIONAL THINKING; AUTOMATED ASSESSMENT; ASSIGNMENTS |
| Value | Count | Frequency (%) |
| thinking | 854 | 7.1% |
| computational | 760 | 6.4% |
| education | 443 | 3.7% |
| students | 337 | 2.8% |
| science | 323 | 2.7% |
| robotics | 313 | 2.6% |
| k-12 | 294 | 2.5% |
| skills | 292 | 2.4% |
| design | 283 | 2.4% |
| mathematics | 206 | 1.7% |
| Other values (1415) | 7849 |
Most occurring characters
| Value | Count | Frequency (%) |
| E | 10770 | 8.9% |
| I | 10249 | 8.4% |
| T | 9781 | 8.0% |
| 9411 | 7.7% | |
| N | 8760 | 7.2% |
| A | 7596 | 6.2% |
| O | 7575 | 6.2% |
| ; | 7322 | 6.0% |
| C | 6687 | 5.5% |
| S | 6591 | 5.4% |
| Other values (30) | 36818 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 121560 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| E | 10770 | 8.9% |
| I | 10249 | 8.4% |
| T | 9781 | 8.0% |
| 9411 | 7.7% | |
| N | 8760 | 7.2% |
| A | 7596 | 6.2% |
| O | 7575 | 6.2% |
| ; | 7322 | 6.0% |
| C | 6687 | 5.5% |
| S | 6591 | 5.4% |
| Other values (30) | 36818 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 121560 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| E | 10770 | 8.9% |
| I | 10249 | 8.4% |
| T | 9781 | 8.0% |
| 9411 | 7.7% | |
| N | 8760 | 7.2% |
| A | 7596 | 6.2% |
| O | 7575 | 6.2% |
| ; | 7322 | 6.0% |
| C | 6687 | 5.5% |
| S | 6591 | 5.4% |
| Other values (30) | 36818 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 121560 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| E | 10770 | 8.9% |
| I | 10249 | 8.4% |
| T | 9781 | 8.0% |
| 9411 | 7.7% | |
| N | 8760 | 7.2% |
| A | 7596 | 6.2% |
| O | 7575 | 6.2% |
| ; | 7322 | 6.0% |
| C | 6687 | 5.5% |
| S | 6591 | 5.4% |
| Other values (30) | 36818 |
abstract_wos
Text
Missing
| Distinct | 4106 |
|---|---|
| Distinct (%) | 99.9% |
| Missing | 6720 |
| Missing (%) | 62.0% |
| Memory size | 5.5 MiB |
Length
| Max length | 22855 |
|---|---|
| Median length | 1736 |
| Mean length | 1300.9599 |
| Min length | 81 |
Unique
| Unique | 4101 ? |
|---|---|
| Unique (%) | 99.8% |
Sample
| 1st row | Troubleshooting is a particular problem-solving process comprising error detection, fault diagnosis, and system restoration. Since automation of systems has become increasingly complex and ubiquitous, troubleshooting skills are crucial to maintain safety and security in a variety of contexts. The planned study aims at examining troubleshooting strategies and how to induce them by means of simple visual aids and concise instructions. To this end, a computerized task consisting of network troubleshooting problems will be employed in an experimental study with repeated measures. Indicators of strategy use and performance will be tested for their relation to availability and differential use of visual aids, to cognitive styles that affect how individuals deal with challenges or system information, and to cognitive processes that are involved in metacognition and executive function. The planned research is expected to help gain insights into the cognitive determinants of troubleshooting, reverse engineering, and their links to computational thinking. |
|---|---|
| 2nd row | This systematic review examines 35 empirical studies featuring the use of think-aloud interviews in computational thinking (CT) research. Findings show that think-aloud interviews (1) are typically conducted in Computer Science classrooms and with K-12 students; (2) are usually combined with other exploratory CT assessment tools; (3) have the potential to benefit learners with special needs and identify the competency gaps through involving diverse participants; (4) are conducted in the absence of cognitive models and standard procedures; and (5) display insufficient definitional and methodological rigor. Theoretically, this review presents a systematic assessment about the application of think-aloud interviews in CT studies and identifies the limitations in existing CT-related think-aloud studies. Practically, this review serves as a reference for studying the cognitive processes during CT problem-solving and provides suggestions for CT researchers who intend to incorporate think-aloud interviews in their studies. |
| 3rd row | Biomedical science students need to learn to code. Graduates face a future where they will be better prepared for research higher degrees and the workforce if they can code. Embedding coding in a biomedical curriculum comes with challenges. First, biomedical science students often experience anxiety learning quantitative and computational thinking skills and second biomedical faculty often lack expertise required to teach coding. In this study, we describe a creative coding approach to building coding skills in students using the packages of Processing and Arduino. Biomedical science students were taught by an interdisciplinary faculty team from Medicine and Health, Science and Architecture, Design and Planning. We describe quantitative and qualitative responses of students to this approach. Cluster analysis revealed a diversity of student responses, with a large majority of students who supported creative coding in the curriculum, a smaller but vocal cluster, who did not support creative coding because either the exercises were not sufficiently challenging or were too challenging and believed coding should not be in a Biomedical Science curriculum. We describe how two creative coding platforms, Processing and Arduino, embedded and used to visualize human physiological data, and provide responses to students, including those minority of students, who are opposed to coding in the curriculum This study found a variety of students responses in a final year capstone course of an undergraduate Biomedical Science degree where future pathways for students are either in research higher degrees or to the workforce with a future which will be increasingly data driven. |
| 4th row | Computational thinking has been recognized as a collection of understandings and skills required for new generations of students not only proficient at using tools, but also at creating them and understanding the implication of their capabilities and limitations. This study proposes the combination of modeling and simulation practices along with disciplinary learning as a way to synergistically integrate and take advantage of computational thinking in engineering education. This paper first proposes a framework that identifies different audiences of computing and related computational thinking practices at the intersection of computer science and engineering. Then, based on a survey with 37 experts from industry and academia, this paper also suggests a series of modeling and simulation practices, methods, and tools for such audiences. Finally, this paper also reports experts' identified challenges and opportunities for integrating modeling and simulation practices at the undergraduate level. (C) 2016 Wiley Periodicals, Inc. |
| 5th row | Much can be learned from the vast work on the use of computer simulations for inquiry learning for the integration of modeling and simulation practices in engineering education. This special issue presents six manuscripts that take steps toward evidence-based teaching and learning practices. These six studies present learning designs that align learning objectives, with evidence of the learning, and pedagogy. Here we highlight the main contributions from each paper individually, but also themes identified across all of them. These themes include (a) approaches for modeling-and-simulation-centric course design; (b) teaching practices and pedagogies for modeling and simulation implementation; and (c) evidence of learning with and about modeling and simulation practices. We conclude our introduction by highlighting desirable characteristics of studies that report on the effectiveness of modeling and simulation in engineering education, and with that we provide some recommendations for improving the scholarship of teaching and learning in this field. |
| Value | Count | Frequency (%) |
| the | 42091 | 5.5% |
| and | 30892 | 4.1% |
| of | 26717 | 3.5% |
| in | 20710 | 2.7% |
| to | 20458 | 2.7% |
| a | 14692 | 1.9% |
| students | 8732 | 1.1% |
| for | 8334 | 1.1% |
| thinking | 7633 | 1.0% |
| that | 7473 | 1.0% |
| Other values (22029) | 573214 |
Most occurring characters
| Value | Count | Frequency (%) |
| 756845 | ||
| e | 516582 | 9.7% |
| t | 399701 | 7.5% |
| i | 370547 | 6.9% |
| n | 353731 | 6.6% |
| a | 337460 | 6.3% |
| o | 313576 | 5.9% |
| s | 297583 | 5.6% |
| r | 261360 | 4.9% |
| c | 189019 | 3.5% |
| Other values (82) | 1551842 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 5348246 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 756845 | ||
| e | 516582 | 9.7% |
| t | 399701 | 7.5% |
| i | 370547 | 6.9% |
| n | 353731 | 6.6% |
| a | 337460 | 6.3% |
| o | 313576 | 5.9% |
| s | 297583 | 5.6% |
| r | 261360 | 4.9% |
| c | 189019 | 3.5% |
| Other values (82) | 1551842 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 5348246 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 756845 | ||
| e | 516582 | 9.7% |
| t | 399701 | 7.5% |
| i | 370547 | 6.9% |
| n | 353731 | 6.6% |
| a | 337460 | 6.3% |
| o | 313576 | 5.9% |
| s | 297583 | 5.6% |
| r | 261360 | 4.9% |
| c | 189019 | 3.5% |
| Other values (82) | 1551842 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 5348246 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 756845 | ||
| e | 516582 | 9.7% |
| t | 399701 | 7.5% |
| i | 370547 | 6.9% |
| n | 353731 | 6.6% |
| a | 337460 | 6.3% |
| o | 313576 | 5.9% |
| s | 297583 | 5.6% |
| r | 261360 | 4.9% |
| c | 189019 | 3.5% |
| Other values (82) | 1551842 |
times_cited_wos_core
Real number (ℝ)
High correlation Missing Skewed Zeros
| Distinct | 156 |
|---|---|
| Distinct (%) | 3.7% |
| Missing | 6639 |
| Missing (%) | 61.3% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 13.628101 |
| Minimum | 0 |
|---|---|
| Maximum | 3737 |
| Zeros | 1047 |
| Zeros (%) | 9.7% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 84.7 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 0 |
| Q1 | 1 |
| median | 3 |
| Q3 | 11 |
| 95-th percentile | 50 |
| Maximum | 3737 |
| Range | 3737 |
| Interquartile range (IQR) | 10 |
Descriptive statistics
| Standard deviation | 72.140259 |
|---|---|
| Coefficient of variation (CV) | 5.2934931 |
| Kurtosis | 1738.7809 |
| Mean | 13.628101 |
| Median Absolute Deviation (MAD) | 3 |
| Skewness | 36.206914 |
| Sum | 57129 |
| Variance | 5204.2169 |
| Monotonicity | Not monotonic |
| Value | Count | Frequency (%) |
| 0 | 1047 | 9.7% |
| 1 | 516 | 4.8% |
| 2 | 358 | 3.3% |
| 3 | 267 | 2.5% |
| 4 | 189 | 1.7% |
| 5 | 188 | 1.7% |
| 6 | 152 | 1.4% |
| 7 | 134 | 1.2% |
| 8 | 96 | 0.9% |
| 9 | 95 | 0.9% |
| Other values (146) | 1150 | 10.6% |
| (Missing) | 6639 |
| Value | Count | Frequency (%) |
| 0 | 1047 | |
| 1 | 516 | |
| 2 | 358 | 3.3% |
| 3 | 267 | 2.5% |
| 4 | 189 | 1.7% |
| 5 | 188 | 1.7% |
| 6 | 152 | 1.4% |
| 7 | 134 | 1.2% |
| 8 | 96 | 0.9% |
| 9 | 95 | 0.9% |
| Value | Count | Frequency (%) |
| 3737 | 1 | |
| 1337 | 1 | |
| 969 | 1 | |
| 816 | 1 | |
| 779 | 1 | |
| 752 | 1 | |
| 545 | 1 | |
| 484 | 1 | |
| 455 | 1 | |
| 384 | 1 |
times_cited_wos_all
Real number (ℝ)
High correlation Missing Skewed Zeros
| Distinct | 184 |
|---|---|
| Distinct (%) | 4.4% |
| Missing | 6639 |
| Missing (%) | 61.3% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 17.158874 |
| Minimum | 0 |
|---|---|
| Maximum | 4918 |
| Zeros | 903 |
| Zeros (%) | 8.3% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 84.7 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 0 |
| Q1 | 1 |
| median | 4 |
| Q3 | 13 |
| 95-th percentile | 62 |
| Maximum | 4918 |
| Range | 4918 |
| Interquartile range (IQR) | 12 |
Descriptive statistics
| Standard deviation | 94.431209 |
|---|---|
| Coefficient of variation (CV) | 5.5033453 |
| Kurtosis | 1777.9325 |
| Mean | 17.158874 |
| Median Absolute Deviation (MAD) | 4 |
| Skewness | 36.780789 |
| Sum | 71930 |
| Variance | 8917.2532 |
| Monotonicity | Not monotonic |
| Value | Count | Frequency (%) |
| 0 | 903 | 8.3% |
| 1 | 510 | 4.7% |
| 2 | 348 | 3.2% |
| 3 | 238 | 2.2% |
| 4 | 204 | 1.9% |
| 5 | 164 | 1.5% |
| 6 | 150 | 1.4% |
| 7 | 138 | 1.3% |
| 9 | 100 | 0.9% |
| 8 | 100 | 0.9% |
| Other values (174) | 1337 | 12.3% |
| (Missing) | 6639 |
| Value | Count | Frequency (%) |
| 0 | 903 | |
| 1 | 510 | |
| 2 | 348 | 3.2% |
| 3 | 238 | 2.2% |
| 4 | 204 | 1.9% |
| 5 | 164 | 1.5% |
| 6 | 150 | 1.4% |
| 7 | 138 | 1.3% |
| 8 | 100 | 0.9% |
| 9 | 100 | 0.9% |
| Value | Count | Frequency (%) |
| 4918 | 1 | |
| 1766 | 1 | |
| 1314 | 1 | |
| 1081 | 1 | |
| 994 | 1 | |
| 918 | 1 | |
| 728 | 1 | |
| 601 | 1 | |
| 545 | 1 | |
| 472 | 1 |
cited_reference_count_wos
Real number (ℝ)
High correlation Missing
| Distinct | 179 |
|---|---|
| Distinct (%) | 4.3% |
| Missing | 6639 |
| Missing (%) | 61.3% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 46.835639 |
| Minimum | 0 |
|---|---|
| Maximum | 678 |
| Zeros | 62 |
| Zeros (%) | 0.6% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 84.7 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 6 |
| Q1 | 22 |
| median | 41.5 |
| Q3 | 64 |
| 95-th percentile | 104 |
| Maximum | 678 |
| Range | 678 |
| Interquartile range (IQR) | 42 |
Descriptive statistics
| Standard deviation | 33.769355 |
|---|---|
| Coefficient of variation (CV) | 0.72101833 |
| Kurtosis | 30.96061 |
| Mean | 46.835639 |
| Median Absolute Deviation (MAD) | 20.5 |
| Skewness | 2.5960496 |
| Sum | 196335 |
| Variance | 1140.3693 |
| Monotonicity | Not monotonic |
| Value | Count | Frequency (%) |
| 33 | 72 | 0.7% |
| 31 | 70 | 0.6% |
| 15 | 64 | 0.6% |
| 39 | 63 | 0.6% |
| 21 | 62 | 0.6% |
| 0 | 62 | 0.6% |
| 29 | 60 | 0.6% |
| 28 | 59 | 0.5% |
| 18 | 57 | 0.5% |
| 58 | 57 | 0.5% |
| Other values (169) | 3566 | |
| (Missing) | 6639 |
| Value | Count | Frequency (%) |
| 0 | 62 | |
| 1 | 22 | 0.2% |
| 2 | 12 | 0.1% |
| 3 | 34 | |
| 4 | 30 | |
| 5 | 45 | |
| 6 | 45 | |
| 7 | 47 | |
| 8 | 44 | |
| 9 | 36 |
| Value | Count | Frequency (%) |
| 678 | 1 | |
| 310 | 1 | |
| 217 | 1 | |
| 208 | 2 | |
| 207 | 1 | |
| 203 | 1 | |
| 201 | 1 | |
| 191 | 1 | |
| 190 | 1 | |
| 189 | 1 |
wos_categories
Text
Missing
| Distinct | 396 |
|---|---|
| Distinct (%) | 9.5% |
| Missing | 6654 |
| Missing (%) | 61.4% |
| Memory size | 661.8 KiB |
Length
| Max length | 258 |
|---|---|
| Median length | 187 |
| Mean length | 62.231506 |
| Min length | 3 |
Unique
| Unique | 192 ? |
|---|---|
| Unique (%) | 4.6% |
Sample
| 1st row | Psychology, Experimental |
|---|---|
| 2nd row | Psychology, Experimental |
| 3rd row | Biochemistry & Molecular Biology; Education, Scientific Disciplines |
| 4th row | Computer Science, Interdisciplinary Applications; Education, Scientific Disciplines; Engineering, Multidisciplinary |
| 5th row | Computer Science, Interdisciplinary Applications; Education, Scientific Disciplines; Engineering, Multidisciplinary |
| Value | Count | Frequency (%) |
| education | 3403 | |
| 3293 | ||
| science | 2772 | |
| computer | 2611 | |
| educational | 2285 | 8.4% |
| research | 2246 | 8.3% |
| scientific | 1155 | 4.3% |
| disciplines | 1155 | 4.3% |
| interdisciplinary | 976 | 3.6% |
| applications | 928 | 3.4% |
| Other values (142) | 6227 |
Most occurring characters
| Value | Count | Frequency (%) |
| i | 26571 | 10.2% |
| 22874 | 8.8% | |
| e | 22226 | 8.6% |
| c | 21990 | 8.5% |
| n | 18806 | 7.2% |
| t | 15396 | 5.9% |
| a | 14629 | 5.6% |
| o | 13188 | 5.1% |
| r | 10659 | 4.1% |
| s | 9521 | 3.7% |
| Other values (37) | 84081 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 259941 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| i | 26571 | 10.2% |
| 22874 | 8.8% | |
| e | 22226 | 8.6% |
| c | 21990 | 8.5% |
| n | 18806 | 7.2% |
| t | 15396 | 5.9% |
| a | 14629 | 5.6% |
| o | 13188 | 5.1% |
| r | 10659 | 4.1% |
| s | 9521 | 3.7% |
| Other values (37) | 84081 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 259941 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| i | 26571 | 10.2% |
| 22874 | 8.8% | |
| e | 22226 | 8.6% |
| c | 21990 | 8.5% |
| n | 18806 | 7.2% |
| t | 15396 | 5.9% |
| a | 14629 | 5.6% |
| o | 13188 | 5.1% |
| r | 10659 | 4.1% |
| s | 9521 | 3.7% |
| Other values (37) | 84081 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 259941 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| i | 26571 | 10.2% |
| 22874 | 8.8% | |
| e | 22226 | 8.6% |
| c | 21990 | 8.5% |
| n | 18806 | 7.2% |
| t | 15396 | 5.9% |
| a | 14629 | 5.6% |
| o | 13188 | 5.1% |
| r | 10659 | 4.1% |
| s | 9521 | 3.7% |
| Other values (37) | 84081 |
research_areas
Text
Missing
| Distinct | 187 |
|---|---|
| Distinct (%) | 4.5% |
| Missing | 6654 |
| Missing (%) | 61.4% |
| Memory size | 554.3 KiB |
Length
| Max length | 140 |
|---|---|
| Median length | 139 |
| Mean length | 35.889155 |
| Min length | 3 |
Unique
| Unique | 108 ? |
|---|---|
| Unique (%) | 2.6% |
Sample
| 1st row | Psychology |
|---|---|
| 2nd row | Psychology |
| 3rd row | Biochemistry & Molecular Biology; Education & Educational Research |
| 4th row | Computer Science; Education & Educational Research; Engineering |
| 5th row | Computer Science; Education & Educational Research; Engineering |
| Value | Count | Frequency (%) |
| 3431 | ||
| research | 2966 | |
| education | 2961 | |
| educational | 2961 | |
| science | 1916 | |
| computer | 1721 | |
| engineering | 426 | 2.3% |
| psychology | 185 | 1.0% |
| other | 157 | 0.9% |
| topics | 157 | 0.9% |
| Other values (109) | 1339 | 7.3% |
Most occurring characters
| Value | Count | Frequency (%) |
| c | 14075 | 9.4% |
| 14043 | 9.4% | |
| e | 13494 | 9.0% |
| a | 12524 | 8.4% |
| n | 10005 | 6.7% |
| i | 9999 | 6.7% |
| o | 9433 | 6.3% |
| t | 8545 | 5.7% |
| u | 7975 | 5.3% |
| E | 6486 | 4.3% |
| Other values (35) | 43330 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 149909 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| c | 14075 | 9.4% |
| 14043 | 9.4% | |
| e | 13494 | 9.0% |
| a | 12524 | 8.4% |
| n | 10005 | 6.7% |
| i | 9999 | 6.7% |
| o | 9433 | 6.3% |
| t | 8545 | 5.7% |
| u | 7975 | 5.3% |
| E | 6486 | 4.3% |
| Other values (35) | 43330 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 149909 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| c | 14075 | 9.4% |
| 14043 | 9.4% | |
| e | 13494 | 9.0% |
| a | 12524 | 8.4% |
| n | 10005 | 6.7% |
| i | 9999 | 6.7% |
| o | 9433 | 6.3% |
| t | 8545 | 5.7% |
| u | 7975 | 5.3% |
| E | 6486 | 4.3% |
| Other values (35) | 43330 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 149909 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| c | 14075 | 9.4% |
| 14043 | 9.4% | |
| e | 13494 | 9.0% |
| a | 12524 | 8.4% |
| n | 10005 | 6.7% |
| i | 9999 | 6.7% |
| o | 9433 | 6.3% |
| t | 8545 | 5.7% |
| u | 7975 | 5.3% |
| E | 6486 | 4.3% |
| Other values (35) | 43330 |
publisher_wos
Text
Missing
| Distinct | 255 |
|---|---|
| Distinct (%) | 6.1% |
| Missing | 6639 |
| Missing (%) | 61.3% |
| Memory size | 495.6 KiB |
Length
| Max length | 74 |
|---|---|
| Median length | 71 |
| Mean length | 21.341842 |
| Min length | 4 |
Unique
| Unique | 129 ? |
|---|---|
| Unique (%) | 3.1% |
Sample
| 1st row | WILEY |
|---|---|
| 2nd row | WILEY |
| 3rd row | WILEY |
| 4th row | WILEY |
| 5th row | WILEY |
| Value | Count | Frequency (%) |
| springer | 896 | 7.4% |
| assoc | 867 | 7.2% |
| computing | 811 | 6.7% |
| machinery | 811 | 6.7% |
| ltd | 639 | 5.3% |
| 506 | 4.2% | |
| ieee | 431 | 3.6% |
| publishing | 367 | 3.0% |
| ag | 365 | 3.0% |
| francis | 338 | 2.8% |
| Other values (478) | 6076 |
Most occurring characters
| Value | Count | Frequency (%) |
| I | 8783 | 9.8% |
| E | 8296 | 9.3% |
| 7915 | 8.8% | |
| N | 7179 | 8.0% |
| S | 6163 | 6.9% |
| R | 6139 | 6.9% |
| A | 5907 | 6.6% |
| C | 5185 | 5.8% |
| O | 4522 | 5.1% |
| T | 4323 | 4.8% |
| Other values (46) | 25053 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 89465 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| I | 8783 | 9.8% |
| E | 8296 | 9.3% |
| 7915 | 8.8% | |
| N | 7179 | 8.0% |
| S | 6163 | 6.9% |
| R | 6139 | 6.9% |
| A | 5907 | 6.6% |
| C | 5185 | 5.8% |
| O | 4522 | 5.1% |
| T | 4323 | 4.8% |
| Other values (46) | 25053 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 89465 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| I | 8783 | 9.8% |
| E | 8296 | 9.3% |
| 7915 | 8.8% | |
| N | 7179 | 8.0% |
| S | 6163 | 6.9% |
| R | 6139 | 6.9% |
| A | 5907 | 6.6% |
| C | 5185 | 5.8% |
| O | 4522 | 5.1% |
| T | 4323 | 4.8% |
| Other values (46) | 25053 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 89465 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| I | 8783 | 9.8% |
| E | 8296 | 9.3% |
| 7915 | 8.8% | |
| N | 7179 | 8.0% |
| S | 6163 | 6.9% |
| R | 6139 | 6.9% |
| A | 5907 | 6.6% |
| C | 5185 | 5.8% |
| O | 4522 | 5.1% |
| T | 4323 | 4.8% |
| Other values (46) | 25053 |
language_wos
Categorical
High correlation Imbalance Missing
| Distinct | 11 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 6639 |
| Missing (%) | 61.3% |
| Memory size | 592.5 KiB |
| English | |
|---|---|
| Spanish | 86 |
| Portuguese | 25 |
| French | 4 |
| German | 3 |
| Other values (6) | 10 |
Length
| Max length | 11 |
|---|---|
| Median length | 7 |
| Mean length | 7.0171756 |
| Min length | 6 |
Unique
| Unique | 3 ? |
|---|---|
| Unique (%) | 0.1% |
Sample
| 1st row | English |
|---|---|
| 2nd row | English |
| 3rd row | English |
| 4th row | English |
| 5th row | English |
Common Values
| Value | Count | Frequency (%) |
| English | 4064 | |
| Spanish | 86 | 0.8% |
| Portuguese | 25 | 0.2% |
| French | 4 | < 0.1% |
| German | 3 | < 0.1% |
| Turkish | 3 | < 0.1% |
| Russian | 2 | < 0.1% |
| Korean | 2 | < 0.1% |
| Unspecified | 1 | < 0.1% |
| Chinese | 1 | < 0.1% |
| (Missing) | 6639 |
Length
| Value | Count | Frequency (%) |
| english | 4064 | |
| spanish | 86 | 2.1% |
| portuguese | 25 | 0.6% |
| french | 4 | 0.1% |
| german | 3 | 0.1% |
| turkish | 3 | 0.1% |
| russian | 2 | < 0.1% |
| korean | 2 | < 0.1% |
| unspecified | 1 | < 0.1% |
| chinese | 1 | < 0.1% |
Most occurring characters
| Value | Count | Frequency (%) |
| s | 4184 | |
| n | 4165 | |
| i | 4160 | |
| h | 4158 | |
| g | 4089 | |
| E | 4064 | |
| l | 4064 | |
| a | 95 | 0.3% |
| p | 87 | 0.3% |
| S | 86 | 0.3% |
| Other values (18) | 264 | 0.9% |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 29416 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| s | 4184 | |
| n | 4165 | |
| i | 4160 | |
| h | 4158 | |
| g | 4089 | |
| E | 4064 | |
| l | 4064 | |
| a | 95 | 0.3% |
| p | 87 | 0.3% |
| S | 86 | 0.3% |
| Other values (18) | 264 | 0.9% |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 29416 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| s | 4184 | |
| n | 4165 | |
| i | 4160 | |
| h | 4158 | |
| g | 4089 | |
| E | 4064 | |
| l | 4064 | |
| a | 95 | 0.3% |
| p | 87 | 0.3% |
| S | 86 | 0.3% |
| Other values (18) | 264 | 0.9% |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 29416 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| s | 4184 | |
| n | 4165 | |
| i | 4160 | |
| h | 4158 | |
| g | 4089 | |
| E | 4064 | |
| l | 4064 | |
| a | 95 | 0.3% |
| p | 87 | 0.3% |
| S | 86 | 0.3% |
| Other values (18) | 264 | 0.9% |
affiliations_wos
Text
Missing
| Distinct | 2580 |
|---|---|
| Distinct (%) | 64.8% |
| Missing | 6848 |
| Missing (%) | 63.2% |
| Memory size | 669.9 KiB |
Length
| Max length | 725 |
|---|---|
| Median length | 266 |
| Mean length | 68.173989 |
| Min length | 5 |
Unique
| Unique | 2014 ? |
|---|---|
| Unique (%) | 50.6% |
Sample
| 1st row | Ruhr University Bochum; Ruhr University Bochum |
|---|---|
| 2nd row | University of Alberta; University of Alberta |
| 3rd row | University of Sydney; University of New South Wales Sydney; University of Sydney; University of Sydney |
| 4th row | Purdue University System; Purdue University; Purdue University System; Purdue University; Instituto Federal da Bahia (IFBA) |
| 5th row | Purdue University System; Purdue University; University of Twente |
| Value | Count | Frequency (%) |
| university | 6809 | 19.8% |
| of | 3864 | 11.2% |
| system | 1013 | 2.9% |
| de | 866 | 2.5% |
| state | 732 | 2.1% |
| universidad | 557 | 1.6% |
| technology | 500 | 1.5% |
| national | 465 | 1.3% |
| 418 | 1.2% | |
| normal | 390 | 1.1% |
| Other values (2089) | 18839 |
Most occurring characters
| Value | Count | Frequency (%) |
| 30470 | 11.2% | |
| i | 26367 | 9.7% |
| e | 21546 | 7.9% |
| n | 19635 | 7.2% |
| a | 16541 | 6.1% |
| t | 16513 | 6.1% |
| r | 14510 | 5.3% |
| o | 14251 | 5.2% |
| s | 13993 | 5.2% |
| y | 9654 | 3.6% |
| Other values (57) | 88057 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 271537 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 30470 | 11.2% | |
| i | 26367 | 9.7% |
| e | 21546 | 7.9% |
| n | 19635 | 7.2% |
| a | 16541 | 6.1% |
| t | 16513 | 6.1% |
| r | 14510 | 5.3% |
| o | 14251 | 5.2% |
| s | 13993 | 5.2% |
| y | 9654 | 3.6% |
| Other values (57) | 88057 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 271537 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 30470 | 11.2% | |
| i | 26367 | 9.7% |
| e | 21546 | 7.9% |
| n | 19635 | 7.2% |
| a | 16541 | 6.1% |
| t | 16513 | 6.1% |
| r | 14510 | 5.3% |
| o | 14251 | 5.2% |
| s | 13993 | 5.2% |
| y | 9654 | 3.6% |
| Other values (57) | 88057 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 271537 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 30470 | 11.2% | |
| i | 26367 | 9.7% |
| e | 21546 | 7.9% |
| n | 19635 | 7.2% |
| a | 16541 | 6.1% |
| t | 16513 | 6.1% |
| r | 14510 | 5.3% |
| o | 14251 | 5.2% |
| s | 13993 | 5.2% |
| y | 9654 | 3.6% |
| Other values (57) | 88057 |
addresses_wos
Text
Missing
| Distinct | 4120 |
|---|---|
| Distinct (%) | 98.5% |
| Missing | 6648 |
| Missing (%) | 61.4% |
| Memory size | 1.2 MiB |
Length
| Max length | 1245 |
|---|---|
| Median length | 468 |
| Mean length | 201.46761 |
| Min length | 38 |
Unique
| Unique | 4065 ? |
|---|---|
| Unique (%) | 97.2% |
Sample
| 1st row | [Bordewieck, Martin; Elson, Malte] Ruhr Univ Bochum, Fac Psychol, Bochum, Germany; [Bordewieck, Martin; Elson, Malte] Ruhr Univ Bochum, Horst Gortz Inst IT Secur, Bochum, Germany |
|---|---|
| 2nd row | [Pan, Zexuan; Cui, Ying; Leighton, Jacqueline P.; Cutumisu, Maria] Univ Alberta, Fac Educ, Ctr Res Appl Measurement & Evaluat, Dept Educ Psychol, Edmonton, AB, Canada; [Cutumisu, Maria] Univ Alberta, Fac Educ, Dept Educ Psychol, 6-102 Educ North, Edmonton, AB T6G 2G5, Canada |
| 3rd row | [Gough, Phillip] Univ Sydney, Affect Interact Lab, Sch Architecture Design & Planning, Camperdown, NSW, Australia; [Bown, Oliver] Univ New South Wales, Fac Art & Design, Kensington, NSW, Australia; [Campbell, Craig R.; Poronnik, Philip] Univ Sydney, Fac Med & Hlth, Sch Med Sci, FMH Media Lab,Educ Innovat, Camperdown, NSW, Australia; [Ross, Pauline M.] Univ Sydney, Fac Sci, Sch Life & Environm Sci, Camperdown, NSW 2006, Australia |
| 4th row | [Magana, Alejandra J.] Purdue Univ, Comp & Informat Technol & Engn Educ, W Lafayette, IN 47906 USA; [Coutinho, Genisson Silva] Purdue Univ, Engn Educ, W Lafayette, IN 47906 USA; [Coutinho, Genisson Silva] Inst Fed Educ Ciencia & Tecnol Bahia, Mech & Mat Technol Dept, Salvador, BA, Brazil |
| 5th row | [Magana, Alejandra J.] Purdue Univ, Dept Comp & Informat Technol, Knoy Hall Bldg,Room 231,401 N Grant St, W Lafayette, IN 47907 USA; [de Jong, Ton] Univ Twente, Fac Behav Management & Social Sci, Enschede, Netherlands |
| Value | Count | Frequency (%) |
| univ | 7279 | 5.9% |
| educ | 3066 | 2.5% |
| 2792 | 2.3% | |
| dept | 2583 | 2.1% |
| usa | 2486 | 2.0% |
| sci | 1859 | 1.5% |
| technol | 1333 | 1.1% |
| sch | 1197 | 1.0% |
| china | 1158 | 0.9% |
| comp | 1137 | 0.9% |
| Other values (19011) | 97633 |
Most occurring characters
| Value | Count | Frequency (%) |
| 118340 | 14.0% | |
| a | 62138 | 7.4% |
| n | 53955 | 6.4% |
| i | 48233 | 5.7% |
| e | 46325 | 5.5% |
| , | 43539 | 5.2% |
| o | 34533 | 4.1% |
| r | 30196 | 3.6% |
| l | 26567 | 3.2% |
| t | 25192 | 3.0% |
| Other values (64) | 353721 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 842739 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 118340 | 14.0% | |
| a | 62138 | 7.4% |
| n | 53955 | 6.4% |
| i | 48233 | 5.7% |
| e | 46325 | 5.5% |
| , | 43539 | 5.2% |
| o | 34533 | 4.1% |
| r | 30196 | 3.6% |
| l | 26567 | 3.2% |
| t | 25192 | 3.0% |
| Other values (64) | 353721 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 842739 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 118340 | 14.0% | |
| a | 62138 | 7.4% |
| n | 53955 | 6.4% |
| i | 48233 | 5.7% |
| e | 46325 | 5.5% |
| , | 43539 | 5.2% |
| o | 34533 | 4.1% |
| r | 30196 | 3.6% |
| l | 26567 | 3.2% |
| t | 25192 | 3.0% |
| Other values (64) | 353721 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 842739 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 118340 | 14.0% | |
| a | 62138 | 7.4% |
| n | 53955 | 6.4% |
| i | 48233 | 5.7% |
| e | 46325 | 5.5% |
| , | 43539 | 5.2% |
| o | 34533 | 4.1% |
| r | 30196 | 3.6% |
| l | 26567 | 3.2% |
| t | 25192 | 3.0% |
| Other values (64) | 353721 |
source_wos
Boolean
Constant Missing
| Distinct | 1 |
|---|---|
| Distinct (%) | < 0.1% |
| Missing | 6639 |
| Missing (%) | 61.3% |
| Memory size | 355.0 KiB |
| True | |
|---|---|
| (Missing) |
| Value | Count | Frequency (%) |
| True | 4192 | |
| (Missing) | 6639 |
Interactions
Correlations
| cited_by | cited_by_scopus | cited_reference_count_wos | document_type | document_type_wos | has_scopus | has_wos | language_scopus | language_wos | publication_type_wos | times_cited_wos_all | times_cited_wos_core | year | year_wos | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cited_by | 1.000 | 1.000 | 0.254 | 0.000 | 0.146 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.965 | 0.960 | -0.436 | -0.468 |
| cited_by_scopus | 1.000 | 1.000 | 0.250 | 0.035 | 0.171 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.960 | 0.959 | -0.438 | -0.486 |
| cited_reference_count_wos | 0.254 | 0.250 | 1.000 | 0.481 | 0.486 | 0.024 | 1.000 | 0.000 | 0.000 | 0.241 | 0.278 | 0.267 | 0.383 | 0.384 |
| document_type | 0.000 | 0.035 | 0.481 | 1.000 | 0.900 | 0.622 | 0.328 | 0.013 | 0.043 | 0.831 | 0.000 | 0.000 | 0.073 | 0.168 |
| document_type_wos | 0.146 | 0.171 | 0.486 | 0.900 | 1.000 | 0.269 | 1.000 | 0.000 | 0.000 | 0.845 | 0.111 | 0.105 | 0.214 | 0.192 |
| has_scopus | 0.000 | 1.000 | 0.024 | 0.622 | 0.269 | 1.000 | 0.336 | 1.000 | 0.158 | 0.255 | 0.000 | 0.000 | 0.101 | 0.000 |
| has_wos | 0.000 | 0.000 | 1.000 | 0.328 | 1.000 | 0.336 | 1.000 | 0.071 | 1.000 | 1.000 | 1.000 | 1.000 | 0.293 | 1.000 |
| language_scopus | 0.000 | 0.000 | 0.000 | 0.013 | 0.000 | 1.000 | 0.071 | 1.000 | 0.794 | 0.003 | 0.000 | 0.000 | 0.000 | 0.023 |
| language_wos | 0.000 | 0.000 | 0.000 | 0.043 | 0.000 | 0.158 | 1.000 | 0.794 | 1.000 | 0.021 | 0.000 | 0.000 | 0.000 | 0.000 |
| publication_type_wos | 0.000 | 0.000 | 0.241 | 0.831 | 0.845 | 0.255 | 1.000 | 0.003 | 0.021 | 1.000 | 0.000 | 0.000 | 0.215 | 0.257 |
| times_cited_wos_all | 0.965 | 0.960 | 0.278 | 0.000 | 0.111 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 0.984 | -0.442 | -0.442 |
| times_cited_wos_core | 0.960 | 0.959 | 0.267 | 0.000 | 0.105 | 0.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.984 | 1.000 | -0.440 | -0.440 |
| year | -0.436 | -0.438 | 0.383 | 0.073 | 0.214 | 0.101 | 0.293 | 0.000 | 0.000 | 0.215 | -0.442 | -0.440 | 1.000 | 0.998 |
| year_wos | -0.468 | -0.486 | 0.384 | 0.168 | 0.192 | 0.000 | 1.000 | 0.023 | 0.000 | 0.257 | -0.442 | -0.440 | 0.998 | 1.000 |
Missing values
Sample
| doi | title | year | journal | document_type | cited_by | has_scopus | has_wos | authors | author_keywords | index_keywords | abstract | cited_by_scopus | references_count_scopus | publisher_scopus | language_scopus | affiliations_scopus | country_scopus | source_scopus | title_wos | year_wos | journal_wos | document_type_wos | publication_type_wos | authors_wos | author_full_names_wos | author_keywords_wos | keywords_plus_wos | abstract_wos | times_cited_wos_core | times_cited_wos_all | cited_reference_count_wos | wos_categories | research_areas | publisher_wos | language_wos | affiliations_wos | addresses_wos | source_wos | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 10.1002/(sici)1096-9128(199601)8:1<47::aid-cpe194>3.0.co;2-9 | Benchmarking the computation and communication performance of the CM-5 | 1996 | Concurrency Practice and Experience | Article | 2.0 | True | False | Dinçer, K.; Bozkus, Z.; Ranka, S.; Fox, G. | NaN | Bandwidth; Calculations; Computational methods; Computer networks; Data communication systems; Distributed computer systems; Mathematical models; Parallel algorithms; Performance; Standards; Synchronization; Topology; Benchmarking; Communication latency; Communication start-up time; Computational processing rate; Control network; Diagnostic network; Gaussian elimination code; Global communication; Point to point communication; Vectorization; Parallel processing systems | Thinking Machines' CM-5 machine is a distributed-memory, message-passing computer. In the paper we devise a performance benchmark for the base and vector units and the data communication networks of the CM-5 machine. We model the communication characteristics such as communication latency and bandwidths of point-to-point and global communication primitives. We show, on a simple Gaussian elimination code, that an accurate static performance estimation of parallel algorithms is possible by using those basic machine properties connected with computation, vectorization, communication and synchronization. Furthermore, we describe the embedding of meshes or hypercubes on the CM-5 fat-tree topology and illustrate the performance results of their basic communication primitives. | 2.0 | Solving Problems on Concurrent Processors, (1988); Hypercube Algorithms with Applications to Image Processing and Pattern Recognition, (1990); Bomans, Luc, Benchmarking the iPSC/2 hypercube multiprocessor, Concurrency Practice and Experience, 1, 1, pp. 3-18, (1989); Proceedings of the Frontiers of Massively Parallel Computation, (1992); Hockney, Roger W., Performance parameters and benchmarking of supercomputers, Parallel Computing, 17, 10-11, pp. 1111-1130, (1991); Kwan, Thomas T., Communication and computation performance of the CM-5, pp. 192-201, (1993); Leiserson, Charles E., Network architecture of the Connection Machine CM-5, pp. 272-285, (1992); Lin, Mengjou, Performance evaluation of the CM-5 interconnection network, pp. 189-198, (1993); Ponnusamy, Ravi, Experimental performance evaluation of the CM-5, Journal of Parallel and Distributed Computing, 19, 3, pp. 192-202, (1993); Bailey, David H., The nas parallel benchmarks, International Journal of High Performance Computing Applications, 5, 3, pp. 63-73, (1991) | John Wiley and Sons Ltd | English | Northeast Parallel Architectures Center, Syracuse University, Syracuse, NY, United States | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 10.1002/(sici)1097-0193(1999)8:2/3<128::aid-hbm10>3.0.co;2-g | Computational modeling of high-level cognition and brain function | 1999 | Human Brain Mapping | Conference paper | 73.0 | True | False | Just, M.A.; Carpenter, P.A.; Varma, S. | 4CAPS; Brain function; PET; T-MRI | brain function; cognition; computer model; computer simulation; conference paper; image processing; imaging system; nuclear magnetic resonance imaging; priority journal; Brain; Cognition; Humans; Magnetic Resonance Imaging; Models, Neurological; Neural Networks (Computer); Thinking | This article describes a computational modeling architecture, 4CAPS, which is consistent with key properties of cortical function and makes good contact with functional neuroimaging results. Like earlier cognitive models such as SOAR, ACT-R, 3CAPS, and EPIC, the proposed cognitive model is implemented in a computer simulation that predicts observable variables such as human response times and error patterns. In addition, the proposed 4CAPS model accounts for the functional decomposition of the cognitive system and predicts fMRI activation levels and their localization within specific cortical regions, by incorporating key properties of cortical function into the design of the modeling system. | 73.0 | Rules of the Mind, (1993); Awh, Edward, Dissociation of Storage and Rehearsal in Verbal Working Memory: Evidence from Positron Emission Tomography, Psychological Science, 7, 1, pp. 25-31, (1996); Agrammatism, (1985); Carpenter, Patricia Ann, Graded functional activation in the visuospatial system with the amount of task demand, Journal of Cognitive Neuroscience, 11, 1, pp. 9-24, (1999); Carpenter, Patricia Ann, What one intelligence test measures: A theoretical account of the processing in the Raven progressive matrices test, Psychological Review, 97, 3, pp. 404-431, (1990); Collins, Allan M., Retrieval time from semantic memory, Journal of Verbal Learning and Verbal Behavior, 8, 2, pp. 240-247, (1969); Rethinking Innateness A Connectionist Perspective on Development, (1996); Human Brain Function, (1997); Gabrieli, John D.E., The role of left prefrontal cortex in language and memory, Proceedings of the National Academy of Sciences of the United States of America, 95, 3, pp. 906-913, (1998); Grafman, Jordan Henry, Similarities and Distinctions among Current Models of Prefrontal Cortical Functions, Annals of the New York Academy of Sciences, 769, 1, pp. 337-368, (1995) | NaN | English | Center for Cognitive Brain Imaging, Carnegie Mellon University, Pittsburgh, PA, United States; Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, United States | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | 10.1002/(sici)1097-0363(199706)24:12<1321::aid-fld562>3.0.co;2-l | Parallel computation of incompressible flows with complex geometries | 1997 | International Journal for Numerical Methods in Fluids | Article | 100.0 | True | False | Johnson, A.A.; Tezduyar, T. | Automobile; Complex geometries; Mesh generation; Parallel flow simulation | Aerodynamics; Automobiles; Computer simulation; Finite element method; Flow interactions; Navier Stokes equations; Parallel processing systems; Incompressible flow; Computational fluid dynamics; air flow; incompressible flow; Navier-Stokes equations; vehicles | We present our numerical methods for the solution of large-scale incompressible flow applications with complex geometries. These methods include a stabilized finite element formulation of the Navier-Stokes equations, implementation of this formulation on parallel architectures such as the Thinking Machines CM-5 and the CRAY T3D, and automatic 3D mesh generation techniques based on Delaunay-Voronoï methods for the discretization of complex domains. All three of these methods are required for the numerical simulation of most engineering applications involving fluid flow. We apply these methods to the simulation of airflow past an automobile and fluid-particle interactions. The simulation of airflow past an automobile is of very large scale with a high level of detail and yielded many interesting airflow patterns which help in understanding the aerodynamic characteristics of such vehicles. © 1997 by John Wiley & Sons, Ltd. | 100.0 | Tezduyar, Tayfun E., Computation of unsteady incompressible flows with the stabilized finite element methods: Space-time formulations, iterative strategies and massively parallel implementations, American Society of Mechanical Engineers, Pressure Vessels and Piping Division (Publication) PVP, 246, pp. 7-24, (1992); Añón, J. C R, Computation of incompressible flows with implicit finite element implementations on the Connection Machine, Computer Methods in Applied Mechanics and Engineering, 108, 1-2, pp. 99-118, (1993); Tezduyar, Tayfun E., Parallel Finite-Element Computation of 3D Flows, Computer, 26, 10, pp. 27-36, (1993); Computational Mechanics 95 Proc Int Conf on Computational Engineering Science, (1995); Pvm Parallel Virtual Machine, (1994); Añón, J. C R, An efficient communications strategy for finite element methods on the Connection Machine CM-5 system, Computer Methods in Applied Mechanics and Engineering, 113, 3-4, pp. 363-387, (1994); Mesh Generation and Update Strategies for Parallel Computation of Flow Problems with Moving Boundaries and Interfaces, (1995); Technical Report, (1995); Aliabadi, Shabrouz K., Parallel fluid dynamics computations in aerospace applications, International Journal for Numerical Methods in Fluids, 21, 10, pp. 783-805, (1995); Tezduyar, Tayfun E., Massively parallel finite element simulation of compressible and incompressible flows, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 157-177, (1994) | John Wiley and Sons Ltd | English | Army HPC Research Center, College of Science and Engineering, Minneapolis, MN, United States | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | 10.1002/(sici)1097-0363(199706)24:12<1353::aid-fld564>3.0.co;2-6 | Parallel finite element simulation of large ram-air parachutes | 1997 | International Journal for Numerical Methods in Fluids | Article | 40.0 | True | False | Kalro, V.; Aliabadi, S.; Garrard, W.; Tezduyar, T.; Mittal, S.; Stein, K. | 3D flow simulations; Parachutes; Parallel computations | Computational fluid dynamics; Computer simulation; Drag; Finite element method; Lift; Mathematical models; Navier Stokes equations; Newtonian flow; Parallel processing systems; Three dimensional computer graphics; Canopy inflation; Ram air parachutes; Parachutes; computer simulation; finite element method; parachutes | In the near future, large ram-air parachutes are expected to provide the capability of delivering 21 ton pay loads from altitudes as high as 25,000 ft. In development and test and evaluation of these parachutes the size of the parachute needed and the deployment stages involved make high-performance computing (HPC) simulations a desirable alternative to costly airdrop tests. Although computational simulations based on realistic, 3D, time-dependent models will continue to be a major computational challenge, advanced finite element simulation techniques recently developed for this purpose and the execution of these techniques on HPC platforms are significant steps in the direction to meet this challenge. In this paper, two approaches for analysis of the inflation and gliding of ram-air parachutes are presented. In one of the approaches the point mass flight mechanics equations are solved with the time-varying drag and lift areas obtained from empirical data. This approach is limited to parachutes with similar configurations to those for which data are available. The other approach is 3D finite element computations based on the Navier-Stokes equations governing the airflow around the parachute canopy and Newton's law of motion governing the 3D dynamics of the canopy, with the forces acting on the canopy calculated from the simulated flow field. At the earlier stages of canopy inflation the parachute is modelled as an expanding box, whereas at the later stages, as it expands, the box transforms to a parafoil and glides. These finite element computations are carried out on the massively parallel supercomputers CRAY T3D and Thinking Machines CM-5, typically with millions of coupled, non-linear finite element equations solved simultaneously at every time step or pseudo-time step of the simulation. © 1997 by John Wiley & Sons, Ltd. | 40.0 | Añón, J. C R, Development testing of large ram air inflated wings, (1993); Garrard, William L., Inflation analysis of ram air inflated gliding parachutes, pp. 186-198, (1995); Aliabadi, Shahrouz K., Parallel finite element computation of the dynamics of large ram air parachutes, pp. 278-293, (1995); Añón, J. C R, SEMI-EMPIRICAL THEORY TO PREDICT THE LOAD-TIME HISTORY OF AN INFLATING PARACHUTE., pp. 177-185, (1984); University of Minnesota Parachute Systems Technology Short Courses, (1994); AIAA Paper 91 0862, (1991); Tezduyar, Tayfun E., Parallel Finite-Element Computation of 3D Flows, Computer, 26, 10, pp. 27-36, (1993); Tezduyar, Tayfun E., Massively parallel finite element simulation of compressible and incompressible flows, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 157-177, (1994); Tezduyar, Tayfun E., A new strategy for finite element computations involving moving boundaries and interfaces-The deforming-spatial-domain/space-time procedure: I. The concept and the preliminary numerical tests, Computer Methods in Applied Mechanics and Engineering, 94, 3, pp. 339-351, (1992); Tezduyar, Tayfun E., A new strategy for finite element computations involving moving boundaries and interfaces-The deforming-spatial-domain/space-time procedure: II. Computation of free-surface flows, two-liquid flows, and flows with drifting cylinders, Computer Methods in Applied Mechanics and Engineering, 94, 3, pp. 353-371, (1992) | John Wiley and Sons Ltd | English | Army HPC Research Center, College of Science and Engineering, Minneapolis, MN, United States; Indian Institute of Technology Kanpur, Kanpur, UP, India; U.S. Army Natick RD and E Center, Natick, MA, United States | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 10.1002/(sici)1097-0363(199706)24:12<1371::aid-fld565>3.0.co;2-7 | Parallel finite element methods for large-scale computation of storm surges and tidal flows | 1997 | International Journal for Numerical Methods in Fluids | Article | 24.0 | True | False | Kashiyama, K.; Saitoh, K.; Behr, M.; Tezduyar, T. | Implicit space-time formulation; Parallel finite element method; Storm surge; Three-step explicit formulation; Tidal flow | Computational fluid dynamics; Computer simulation; Finite element method; Parallel processing systems; Tides; Tidal flows; Unstructured grid formulations; Storms; computer simulation; finite element method; storms; tidal flows | Massively parallel finite element methods for large-scale computation of storm surges and tidal flows are discussed here. The finite element computations, carried out using unstructured grids, are based on a three-step explicit formulation and on an implicit space-time formulation. Parallel implementations of these unstructured grid-based formulations are carried out on the Fujitsu Highly Parallel Computer AP1000 and on the Thinking Machines CM-5. Simulations of the storm surge accompanying the Ise-Bay typhoon in 1959 and of the tidal flow in Tokyo Bay serve as numerical examples. The impact of parallelization on this type of simulation is also investigated. The present methods are shown to be useful and powerful tools for the analysis of storm surges and tidal flows. © 1997 by John Wiley & Sons, Ltd. | 24.0 | Añón, J. C R, Tide and storm surge predictions using finite element model, Journal of Hydraulic Engineering, 118, 10, pp. 1373-1390, (1992); Añón, J. C R, Massively parallel finite element method for large scale computation of storm surge, 2, pp. 79-86, (1996); Añón, J. C R, Three‐step explicit finite element computation of shallow water flows on a massively parallel computer, International Journal for Numerical Methods in Fluids, 21, 10, pp. 885-900, (1995); Añón, J. C R, Selective lumping finite element method for shallow water flow, International Journal for Numerical Methods in Fluids, 2, 1, pp. 89-112, (1982); Tezduyar, Tayfun E., Computation of unsteady incompressible flows with the stabilized finite element methods: Space-time formulations, iterative strategies and massively parallel implementations, American Society of Mechanical Engineers, Pressure Vessels and Piping Division (Publication) PVP, 246, pp. 7-24, (1992); Hughes, Thomas J.R., A multi-dimensioal upwind scheme with no crosswind diffusion., 34 )., pp. 19-35, (1979); Tezduyar, Tayfun E., FINITE ELEMENT FORMULATIONS FOR CONVECTION DOMINATED FLOWS WITH PARTICULAR EMPHASIS ON THE COMPRESSIBLE EULER EQUATIONS., (1983); Añón, J. C R, Finite element computation of compressible flows with the SUPG formulation, American Society of Mechanical Engineers, Fluids Engineering Division (Publication) FEDSM, 123, pp. 21-27, (1991); Añón, J. C R, SUPG finite element computation of compressible flows with the entropy and conservation variables formulations, Computer Methods in Applied Mechanics and Engineering, 104, 3, pp. 397-422, (1993); Aliabadi, Shabrouz K., SUPG finite element computation of viscous compressible flows based on the conservation and entropy variables formulations, Computational Mechanics, 11, 5-6, pp. 300-312, (1993) | John Wiley and Sons Ltd | English | Department of Civil Engineering, Chuo University, Hachioji, Tokyo, Japan; University of Minnesota Twin Cities, Minneapolis, MN, United States | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 5 | 10.1002/(sici)1097-0363(199706)24:12<1417::aid-fld567>3.0.co;2-n | Parallel finite element computation of missile aerodynamics | 1997 | International Journal for Numerical Methods in Fluids | Article | 5.0 | True | False | Sturek, W.B.; Ray, S.; Aliabadi, S.; Waters, C.; Tezduyar, T. | Compressible flows; Missile aerodynamics; Parallel computing methods | Algorithms; Compressible flow; Computational fluid dynamics; Finite element method; Mathematical models; Navier Stokes equations; Parallel processing systems; Reynolds number; Supersonic aerodynamics; Turbulence; Viscous flow; Thinking machines; Turbulence models; Missiles; aerodynamics; computational fluid dynamics; finite element method; missiles; Navier-Stokes equations | A flow simulation tool, developed by the authors at the Army HPC Research Center, for compressible flows governed by the Navier-Stokes equations is used to study missile aerodynamics at supersonic speeds, high angles of attack and for large Reynolds numbers. The goal of this study is the evaluation of this Navier-Stokes computational technique for the prediction of separated flow fields around high-length-to-diameter (L/D) bodies. In particular, this paper addresses two issues: (i) turbulence modelling with a finite element computational technique and (ii) efficient performance of the computational technique on two different multiprocessor mainframes, the Thinking Machines CM-5 and CRAY T3D. The paper first provides a discussion of the Navier-Stokes computational technique and the algorithm issues for achieving efficient performance on the CM-5 and T3D. Next, comparisons are shown between the computation and experiment for supersonic ramp flow to evaluate the suitability of the turbulence model. Following that, results of the computations for missile flow fields are shown for laminar and turbulent viscous effects. © 1997 by John Wiley & Sons, Ltd. | 5.0 | Parallel Finite Element Computations in Aerospace Applications, (1994); Advances in Numerical Simulation of Turbulent Flows, (1991); Añón, J. C R, The numerical computation of turbulent flows, Computer Methods in Applied Mechanics and Engineering, 3, 2, pp. 269-289, (1974); AIAA Paper 92 0670, (1983); A One Equation Turbulence Transport Model for High Reynolds Number Wall Bounded Flows, (1990); AIAA Paper 93 3099, (1993); Aliabadi, Shabrouz K., Parallel fluid dynamics computations in aerospace applications, International Journal for Numerical Methods in Fluids, 21, 10, pp. 783-805, (1995); Añón, J. C R, Implementation of implicit finite element methods for incompressible flows on the CM-5, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 95-111, (1994); Computational Mechanics 95 Proc Int Conf on Computational Engineering Science, (1995); Solving Large Scale Problems in Mechanics Parallel and Distributed Computer Applications, (1997) | John Wiley and Sons Ltd | English | U.S. Army Research Laboratory, Adelphi, MD, United States; College of Science and Engineering, Minneapolis, MN, United States | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 6 | 10.1002/(sici)1097-0363(199706)24:12<1449::aid-fld569>3.0.co;2-8 | Parallel finite element calculation of flow in a three-dimensional lid-driven cavity using the CM-5 and T3D | 1997 | International Journal for Numerical Methods in Fluids | Article | 17.0 | True | False | Yeckel, A.; Smith, J.W.; Derby, J.J. | Flow; Incompressible; Parallel finite element; Steady; Three-dimensional | Bifurcation (mathematics); Cavitation; Convergence of numerical methods; Finite element method; Parallel processing systems; Reynolds number; Lid driven cavities; Computational fluid dynamics; cavities; finite element method; steady flow | Steady flows in a three-dimensional lid-driven cavity at moderate Reynolds number are studied using various methods of parallel programming on the Cray T3D and Thinking Machines CM-5. These three-dimensional flows are compared with flows computed in a two-dimensional cavity. Solutions at Reynolds number up to 500 agree well with the experimental data of Aidun et al. (Phys. Fluids A, 3, 2081-2091 (1991)) for the location of separation of the secondary eddy at the downstream wall. Convergence of the three-dimensional problem using GMRES with diagonal preconditioning could not be obtained at Reynolds number greater than about 500. We speculate that the source of the difficulty is the loss of stability via pitchfork and Hopf bifurcations identified by Aidun et al. The relative performance of various methods of message passing on the Cray T3D is compared with the data-parallel mode of programming on the CM-5. No clear advantage between machines or message-passing methods is distinguished. © 1997 by John Wiley & Sons, Ltd. | 17.0 | Añón, J. C R, Theoretical Modeling of Czochralski Crystal Growth, MRS Bulletin, 13, 10, pp. 29-35, (1988); Science and Technology of Crystal Growth, (1995); Science and Technology of Crystal Growth, (1995); Añón, J. C R, Large-scale numerical analysis of materials processing systems: High-temperature crystal growth and molten glass flows, Computer Methods in Applied Mechanics and Engineering, 112, 1-4, pp. 69-89, (1994); Añón, J. C R, Massively parallel finite element computations of three-dimensional, time-dependent, incompressible flows in materials processing systems, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 139-156, (1994); Añón, J. C R, Massively parallel finite element analysis of coupled, incompressible flows: A benchmark computation of baroclinic annulus waves, International Journal for Numerical Methods in Fluids, 21, 10, pp. 1007-1014, (1995); Añón, J. C R, Three-dimensional melt flows in Czochralski oxide growth: high-resolution, massively parallel, finite element computations, Journal of Crystal Growth, 152, 3, pp. 169-181, (1995); Añón, J. C R, On the effects of ampoule tilting during vertical Bridgman growth: Three-dimensional computations via a massively parallel, finite element method, Journal of Crystal Growth, 167, 1-2, pp. 292-304, (1996); Añón, J. C R, Analytical and numerical studies of the structure of steady separated flows, Journal of Fluid Mechanics, 24, 1, pp. 113-151, (1966); Añón, J. C R, High-Re solutions for incompressible flow using the Navier-Stokes equations and a multigrid method, Journal of Computational Physics, 48, 3, pp. 387-411, (1982) | John Wiley and Sons Ltd | English | College of Science and Engineering, Minneapolis, MN, United States; College of Science and Engineering, Minneapolis, MN, United States | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 7 | 10.1002/(sici)1098-111x(199702)12:2<105::aid-int1>3.0.co;2-u | A knowledge level analysis of taxonomic domains | 1997 | International Journal of Intelligent Systems | Article | 5.0 | True | False | Domingo, M.; Sierra, C. | NaN | Artificial intelligence; Computational methods; Computer software; Knowledge based systems; Knowledge representation; Programming theory; Domain models; Knowledge level theory; Knowledge engineering | The Knowledge Level (KL) is an abstract level of description, prior to the symbol or software level, which aims at discovering the components of expertise without thinking of computational aspects. The KL analysis emphasizes the regularities in knowledge use for knowledge engineering. We consider the knowledge level analysis the AI counterpart of the specification of programs. Then, it must be possible to define formal ways of putting in relation the KL analysis with computational elements that implement it. The ultimate goal of the research presented in this article is to contribute in the filling of the gap between specification at the KL and implementation. To do so we propose (i) a particular interpretation of the three main concepts involved in the knowledge level theories, i.e., tasks, methods, and domain models, and (ii) a mapping between these notions and computational elements of Milord II, a shell developed at the IIIA Institute and used as the target programming environment of an example in biological identification. © 1997 John Wiley & Sons, Inc. | 5.0 | Añón, J. C R, The knowledge level, Artificial Intelligence, 18, 1, pp. 87-127, (1982); Clancey, William J., Heuristic classification, Artificial Intelligence, 27, 3, pp. 289-350, (1985); Añón, J. C R, Design problem solving. A task analysis, AI Magazine, 11, 4, pp. 59-71, (1990); Añón, J. C R, Components of expertise, AI Magazine, 11, 2, pp. 28-49, (1990); Second Generation Expert Systems, (1993); Añón, J. C R, KADS: a modelling approach to knowledge engineering, Knowledge Acquisition, 4, 1, pp. 5-53, (1992); Cc AI, (1993); Qualitative Reasoning and Decision Technologies, (1993); Sponges in Time and Space, (1994); Añón, J. C R, Managing linguistically expressed uncertainty in milord application to medical diagnosis, AI Communications, 1, 1, pp. 14-31, (1988) | John Wiley and Sons Inc. | English | CSIC - Instituto de Investigación en Inteligencia Artificial (IIIA), Cerdanyola del Valles, Barcelona, Spain | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 8 | 10.1002/(sici)1098-111x(199911)14:11<1071::aid-int1>3.0.co;2-j | Computational verb systems: verb logic | 1999 | International Journal of Intelligent Systems | Article | 10.0 | True | False | Yang, T. | NaN | Brain; Computational methods; Formal logic; Speech processing; Computational verb logic; Human natural languages; Verb logic; Verbs; Artificial intelligence | Computational verb systems are new platforms for artificial intelligence. They embed the dynamical knowledge expressed by dynamical experiences of human brains into machines by using pattern thinking. In this paper, the relationship defined by verbs in human natural languages is modeled by computational verb logic (verb logic for short). To unify different verbs with different contexts into comparable standard forms, the concepts of BE transformation and canonical BE transformation are given. The atomic and molecular verb sentences under canonical BE transformations are also defined for verb logic. The basic verb logic operations are given. Some examples are given to demonstrate the concepts of BE transformation and verb logic. | 10.0 | Brain and Intelligence in Vertebrates, (1982); Three Pound Universe, (1986); Emperor S New Mind, (1989); English Verb Classes and Alternations A Preliminary Investigation, (1993); Technical Report Memorandum no Ucb Erl M97 66, (1997); Inform Sci, (1998); Internat J Gen Systems, (1998); Elements of English Grammar, (1901); Logic and Boolean Algebra, (1962); Logic and Algorithms, (1966) | John Wiley & Sons Inc | English | University of California, Berkeley, Berkeley, CA, United States | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 9 | 10.1002/(sici)1099-1743(200003/04)17:2<149::aid-sres290>3.0.co;2-q | Systems research, genetic algorithms and information systems | 2000 | Systems Research and Behavioral Science | Article | 53.0 | True | False | Chaudhry, S.S.; Varano, M.W.; Xu, L. | Computational intelligence; Evolution; Genetic algorithms; Information systems; Intelligent systems; Soft computing; Systems science; Systems thinking | NaN | Darwinian evolution and genetics have spawned a class of computational methods called evolutionary algorithms, and in particular, genetic algorithms. These evolutionary strategies provide new opportunities and challenges with ever-increasing applications in industry. In this paper, we propose that the proper context for a basic unifying theory of evolution for the emerging debate on the similarities and differences between biotic evolution and evolutionary algorithms is systems science. Recent changes in technology, coupled with developments in the field of artificial intelligence, promote the growth of enabling technologies, such as intelligent systems, in which we integrate genetic algorithms. Genetic algorithms are integrated with other artificial intelligence tools using a cooperating intelligent subsystem, which is integrated into the information systems of the organization. A portfolio of examples illustrating the evolving and expanding applications of genetic algorithms is included, as well as our computational experience with several commercially available genetic algorithm software. Copyright © 2000 John Wiley & Sons, Ltd. | 53.0 | Management Science, (1971); AI Expert, (1994); Information and Economic Behavior, (1973); Back, Barbro, Neural networks and genetic algorithms for bankruptcy predictions, Expert Systems with Applications, 11, 4 SPEC. ISS., pp. 407-413, (1996); Knowledge Link how Firms Compete Through Strategic Alliances, (1991); Genetic Programming an Introduction, (1998); American Economic Review, (1966); Stern Information Systems Review, (1993); Chatterjee, Sangit, Genetic algorithms in statistics: Procedures and applications, Communications in Statistics Part B: Simulation and Computation, 26, 4, pp. 1617-1630, (1997); Cors Informs National Meeting at Montreal, (1998) | John Wiley and Sons Ltd | English | Department of Decision and Information Technologies, Villanova University, Villanova, PA, United States; Department of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China; Department of Decision and Information Technologies, Villanova University, Villanova, PA, United States | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
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| 10821 | 10.7659/j.issn.1005-6947.2021.02.006 | Analysis of key protein regulatory genes in differential expression profile of gallbladder cancer based on bioinformatics approaches; 基于生物信息学的胆囊癌差异表达谱中关键蛋白调控基因分析 | 2021 | China Journal of General Surgery | Article | 3.0 | True | False | Han, W.; Xin, W.; Su, S.; Wang, Q. | Computational Biology; Gallbladder Neoplasms; Gene Expression Profiling; Protein Interaction Maps | NaN | Background and Aims: The underlying mechanism for the occurrence of gallbladder carcinoma (GBC) is still unclear at present. The available data at the genomic and transcriptomic levels provide the basic data source for investigation of the molecular biological mechanisms of GBC. Therefore, this study was conducted to to analyze the differentially expressed genes in and normal gallbladder tissues and key protein regulatory molecules in GBC by bioinformatics approaches, so as to explore the potential molecular biological mechanism of GBC. Methods: The differentially expressed genes were screened based on two GBC transcriptional datasets from GEO database, and the three GO functional annotations were performed on these genes. The STRING database was applied to construct a protein interaction network, and perform module mining in the investigation of key protein regulatory genes. Finally, the expression and predictive efficacy of the identified key protein regulatory genes were comprehensively evaluated. Results: A total of 140 repeatable differentially expressed genes (20 up-regulated genes and 120 down-regulated genes) in GBC were screened, which are mainly related to the forebrain development and positive regulation of neurogenesis, and participate in the composition of the postsynaptic membrane and transverse tubules. Meanwhile, the SFRP1, a key protein regulatory molecule, had a certain ability in predicting the occurrence of GBC. Conclusion: The information expressed by transcription spectrum of GBC obtained in this study can provide framework and thinking structure for studying the molecular mechanism of GBC. The key protein regulatory molecule SFRP1 probably plays a pivotal role in the occurrence and development of GBC. © Chinese Journal of General Surgery 2021. | 3.0 | Miller, Kimberly D., Cancer treatment and survivorship statistics, 2016, Ca-A Cancer Journal for Clinicians, 66, 4, pp. 271-289, (2016); Hundal, Rajveer, Gallbladder cancer: Epidemiology and outcome, Clinical Epidemiology, 6, 1, pp. 99-109, (2014); Siegel, Rebecca L., Cancer statistics for Hispanics/Latinos, 2015, Ca-A Cancer Journal for Clinicians, 65, 6, pp. 457-480, (2015); Misra, S., Carcinoma of the gallbladder, The Lancet Oncology, 4, 3, pp. 167-176, (2003); Samuel, Sandeep, Clinicopathological characteristics and outcomes of rare histologic subtypes of gallbladder cancer over two decades: A population-based study, PLOS ONE, 13, 6, (2018); Roa, Juan C., Squamous cell and adenosquamous carcinomas of the gallbladder: Clinicopathological analysis of 34 cases identified in 606 carcinomas, Modern Pathology, 24, 8, pp. 1069-1078, (2011); Reid, Kaye M., Diagnosis and surgical management of gallbladder cancer: A review, Journal of Gastrointestinal Surgery, 11, 5, pp. 671-681, (2007); Henley, S. Jane, Gallbladder cancer incidence and mortality, United States 1999-2011, Cancer Epidemiology Biomarkers and Prevention, 24, 9, pp. 1319-1326, (2015); Kakaei, Farzad, Surgical treatment of gallbladder carcinoma: a critical review, Updates in Surgery, 67, 4, pp. 339-351, (2015); Lazcano-Ponce, Eduardo César, Epidemiology and molecular pathology of gallbladder cancer, Ca-A Cancer Journal for Clinicians, 51, 6, pp. 349-364, (2001) | Central South University | Chinese | Department of General Surgery, Air Force Medical University, Xi'an, Shaanxi, China | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 10822 | 10.7764/disena.21.article.7 | Generative Allegories of Oppression and Emancipation: Reflecting with Computational Social Models; Alegorías generativas de opresión y emancipación: Reflexionando con modelos sociales computacionales | 2022 | Disena | Article | 0.0 | True | False | Sosa, R. | Change agents; Creative research methods; Emergence; Multi-agent simulations; Thought experiments | NaN | AThis paper presents a computational approach to growing artificial societies (agent-based simulations) as an explicit, accessible, and systematic tool to visualize and generate insights and new questions about Paulo Freire’s concepts of oppression and emancipation. These models do not make claims of validity or prediction, instead, their value is to structure our thinking and support our understanding. Here, I use computational social simulationas generative allegories to reflect upon the role of designers in participatory, co-design, and social design contexts. The paper shows how Freirean ideas can help reframe design as a pedagogical craft based on dialogue and collective inquiry. © 2022, Pontificia Universidad Catolica de Chile. All rights reserved. | 0.0 | Design Issues, (2004); Axelrod, Robert M., The dissemination of culture: A model with local convergence and global polarization, Journal of Conflict Resolution, 41, 2, pp. 203-226, (1997); Bertolotti, Francesco, Sensitivity to Initial Conditions in Agent-Based Models, Lecture Notes in Computer Science, 12520 LNAI, pp. 501-508, (2020); Social Agents Ecology Exchange and Evolution, (2002); On Anarchism, (2013); Costopoulos, André, How did sugarscape become a whole society model?, 7, pp. 259-269, (2015); Intuition Pumps and Other Tools for Thinking, (2013); Epstein, Joshua M., Agent-based computational models and generative social science, Complexity, 4, 5, pp. 41-60, (1999); Designs for the Pluriverse Radical Interdependence Autonomy and the Making of Worlds, (2018); Pedagogy of the Oppressed, (1970) | Pontificia Universidad Catolica de Chile | English | Auckland University of Technology, Auckland, AUK, New Zealand; Design & Architecture, Monash University, Melbourne, VIC, Australia | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 10823 | 10.7765/9781526142290 | International law in Europe, 700–1200 | 2022 | NaN | Book | 8.0 | True | False | Benham, J. | arbitration; expulsion; International law; legal justification; redress; treaties | Computational methods; Information systems; Information use; Kyoto Protocol; Law enforcement; Arbitration; Expulsion; Legal justification; Legal rules; Middle ages; Redress; International law | "It is the contention of this book that there was a notion of international law in the medieval period, and more specifically in the period 700 to 1200. It examines and analyses the ways and the extent to which such as system of rules was known and followed in the Middle Ages by exploring treaties as the main source of international law, and by following a known framework of evidencing it: that it was practised on a daily basis; that there was a reliance upon justification of action; that the majority of international legal rules were consistently obeyed; and finally, that it had the function to resolve disputed questions of fact and law. This monograph further considers problems such as enforcement, deterrence, authority, and jurisdiction, considering carefully how they can be observed in the medieval evidence, and challenging traditional ideas over their role and function in the history of international law. This monograph then, attempts to make a leap forward in thinking about how rulers, communities, and political entities conducted diplomacy and regulated their interactions with each other in a period before fully fledged nation states. © Jenny Benham 2022. | 8.0 | undefined; undefined; undefined; undefined; undefined; Actes Concernant Les Vicomtes De Marseille Et Leurs Descendants, (1926); Acts of Welsh Rulers 1120 1283, (2005); undefined, (1991); undefined, (1969); Alcuin of York His Life and Letters, (1974) | Manchester University Press | English | NaN | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 10824 | 10.7771/1541-5015.1754 | Tinkering with logo in an elementary mathematics methods course | 2018 | Interdisciplinary Journal of Problem-based Learning | Article | 4.0 | True | False | Valentine, K.D. | Computational literacy; Geometry; Mathematics education; Teacher education | NaN | With an increased push to integrate coding and computational literacy in K–12 learning environments, teacher educators will need to consider ways they might support preservice teachers (PSTs). This paper details a tinkering approach used to engage PSTs in thinking computationally as they worked with geometric concepts they will be expected to teach in K–5. Experiences programming in Logo to construct authentic artifacts in the form of two-dimensional geometric graphics not only supported PSTs’ understanding of core geometric and spatial concepts, but also helped them to make connections between mathematics and computational literacy. Artifacts and discourse are discussed as they relate to three core considerations: engaging learners to construct authentic artifacts, supporting a communitarian ethos, and supporting various types of rapid feedback. © 2018, Purdue University Press. All rights reserved. | 4.0 | Call for Manuscripts Special Issue Tinkering in Technology Rich Design Contexts, (2017); Barr, Valerie B., Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community?, ACM Inroads, 2, 1, pp. 48-54, (2011); Berland, Matthew W., Making, tinkering, and computational literacy, pp. 196-205, (2016); Berland, Matthew W., Using Learning Analytics to Understand the Learning Pathways of Novice Programmers, Journal of the Learning Sciences, 22, 4, pp. 564-599, (2013); Creative Computing, (2014); Proceedings of the 2012 Annual Meeting of the American Educational Research Association, (2012); Common Core State Standards for Mathematics, (2010); Focus in High School Mathematics Technology to Support Reasoning and Sense Making, (2011); Changing Minds Computers Learning and Literacy, (2000); Grover, Shuchi, Computational Thinking in K-12: A Review of the State of the Field, Educational Researcher, 42, 1, pp. 38-43, (2013) | Purdue University Press | English | West Virginia University, Morgantown, WV, United States | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 10825 | 10.7771/2157-9288.1296 | Making makers: Tracing stem identity in rural communities | 2021 | Journal of Pre-College Engineering Education Research | Article | 10.0 | True | False | Nixon, J.; Stoiber, A.; Halverson, E. | Computational thinking; Engineering; Identity; Informal education; Makerspaces; Rural education; STEM | NaN | In this article, we describe efforts to reduce barriers of entry to pre-college engineering in a rural community by training local teens to become maker-mentors and staff a mobile makerspace in their community. We bring a communities of practice frame to our inquiry, focusing on inbound and peripheral learning and identity trajectories as a mechanism for representing the maker-mentor experience. Through a longitudinal case study, we traced the individual trajectories of five maker-mentors over two years. We found a collection of interrelated factors present in those students who maintained inbound trajectories and those who remained on the periphery. Our research suggests that the maker-mentors who facilitated events in the community, taught younger community members about making, and co-facilitated with other maker-mentors were more likely to have inbound trajectories. We offer lessons learned from including a mentorship component in a pre-college maker program, an unusual design feature that afforded more opportunities to create inbound trajectories. A key affordance of the maker-mentor program was that it allowed teens to explore areas of making that were in line with their interests while still being a part of a larger community of practice. Understanding learning and identity trajectories will allow us to continually improve pre-college engineering programming and education opportunities that build on students’ funds of knowledge. © 2021, Purdue University Press. All rights reserved. | 10.0 | Maker Centered Learning and the Development of Self Preliminary Findings of the Agency by Design Project, (2015); Missing Makers how to Rebuild Americas Manufacturing Workforce; Crec Works Newsletter, (2008); Journal of Research in Rural Education, (2011); Barajas-López, Filiberto, Indigenous Making and Sharing: Claywork in an Indigenous STEAM Program, Equity and Excellence in Education, 51, 1, pp. 7-20, (2018); Makeology Makers as Learners, (2016); Barron, Brigid J.S., Predictors of creative computing participation and profiles of experience in two Silicon Valley middle schools, Computers and Education, 54, 1, pp. 178-189, (2010); Makeology Makers as Learners, (2016); Social Justice Education for Teachers Paulo Freire and the Possible Dream, (2008); Blikstein, Paulo, Children are not hackers: Building a culture of powerful ideas, deep learning, and equity in the maker movement, pp. 64-79, (2016) | Purdue University Press | English | PBS Wisconsin Education, United States; University of Wisconsin-Madison, Madison, WI, United States | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 10826 | 10.7821/naer.2021.7.640 | Investigating the Computational Thinking Ability of Young School Students Across Grade Levels in Two Different Types of Romanian Educational Institutions | 2021 | Journal of New Approaches in Educational Research | Article | 7.0 | True | True | Kátai, Z.; Osztián, E.; Lörincz, B. | ALGORITHMS; COMPUTER-ASSISTED INSTRUCTION; CURRICULUM; EDUCATIONAL TESTING; GENDER ROLES | NaN | Over the last decade, continuous efforts have been made to bring computational thinking (CT) closer to K-12 education. These focused endeavors implicitly suggest that the current curricula do not sufficiently contribute to the development of learners’ CT. On the other hand, since CT is a combined skill with cross-disciplinary implications, one might conclude that even without an explicit focus on CS education, students’ CT might develop latently as they advance with the current curriculum. We have proposed to test whether differences exist in how 3rd-, 5th-, 7th- and 9th-grade learners from two Romanian educational institutions (girls vs. boys from Art vs. Theoretical school; 214 subjects with no prior experience in CT) relate to learning tasks that require a certain level of CT. The testing tool was inspired by the AlgoRythmics dance choreography illustration of the linear search algorithm and has the potential to reveal different levels of abstracting. Findings emphasize the need for a purposeful and coordinated CS infusion into K-9 education in order to accelerate students’ CT development. © 2021. The Author(s). | 7.0 | Ackerman, Phillip L., The locus of adult intelligence: Knowledge, abilities, and nonability traits, Psychology and Aging, 14, 2, pp. 314-330, (1999); Ahadi, Alireza, Performance and consistency in learning to program, ACM International Conference Proceeding Series, pp. 11-16, (2017); Aho, Alfred V., Computation and computational thinking, Computer Journal, 55, 7, pp. 833-835, (2012); Atmatzidou, Soumela, Advancing students' computational thinking skills through educational robotics: A study on age and gender relevant differences, Robotics and Autonomous Systems, 75, pp. 661-670, (2016); Brackmann, Christian Puhlmann, Development of computational thinking skills through unplugged activities in primary school, ACM International Conference Proceeding Series, pp. 65-72, (2017); Brown, Neil C.C., Restart: The resurgence of computer science in UK schools, ACM Transactions on Computing Education, 14, 2, (2014); Byrne, Pat, The effect of student attributes on success in programming, pp. 49-52, (2001); Csta K 12 Computer Science Standards, (2017); del Olmo-Muñoz, Javier, Computational thinking through unplugged activities in early years of Primary Education, Computers and Education, 150, (2020); Denning, Peter J., The profession of IT: Beyond computational thinking, Communications of the ACM, 52, 6, pp. 28-30, (2009) | Universidad de Alicante | English | Department of Mathematics and Computer Science, Sapientia Erdélyi Magyar Tudományegyetem, Cluj Napoca, Cluj, Romania | NaN | True | Investigating the Computational Thinking Ability of Young School Students Across Grade Levels in Two Different Types of Romanian Educational Institutions | 2021.0 | JOURNAL OF NEW APPROACHES IN EDUCATIONAL RESEARCH | Article | J | Katai, Z; Osztian, E; Lorincz, B | Katai, Zoltan; Osztian, Erika; Lorincz, Beata | COMPUTER-ASSISTED INSTRUCTION; ALGORITHMS; CURRICULUM; EDUCATIONAL TESTING; GENDER ROLES | GENDER | Over the last decade, continuous efforts have been made to bring computational thinking (CT) closer to K-12 education. These focused endeavors implicitly suggest that the current curricula do not sufficiently contribute to the development of learners' CT. On the other hand, since CT is a combined skill with cross-disciplinary implications, one might conclude that even without an explicit focus on CS education, students' CT might develop latently as they advance with the current curriculum. We have proposed to test whether differences exist in how 3rd-, 5th-, 7th- and 9th-grade learners from two Romanian educational institutions (girls vs. boys from Art vs. Theoretical school; 214 subjects with no prior experience in CT) relate to learning tasks that require a certain level of CT. The testing tool was inspired by the AlgoRythmics dance choreography illustration of the linear search algorithm and has the potential to reveal different levels of abstracting. Findings emphasize the need for a purposeful and coordinated CS infusion into K-9 education in order to accelerate students' CT development. | 9.0 | 10.0 | 40.0 | Education & Educational Research | Education & Educational Research | UNIV ALICANTE, GRUPO INVESTIGACION EDUTIC-ADEI | English | Sapientia Hungarian University of Transylvania | [Katai, Zoltan; Osztian, Erika; Lorincz, Beata] Sapientia Hungarian Univ Transylvania, Dept Math & Informat, Cluj Napoca, Romania | True |
| 10827 | 10.9779/pauefd.1438401 | An Overview of the Epistemological Link between Mathematical Thinking and Computational Thinking from Theory to Practice | 2025 | PAMUKKALE UNIVERSITESI EGITIM FAKULTESI DERGISI-PAMUKKALE UNIVERSITY JOURNAL OF EDUCATION | Article | 1.0 | False | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | An Overview of the Epistemological Link between Mathematical Thinking and Computational Thinking from Theory to Practice | 2025.0 | PAMUKKALE UNIVERSITESI EGITIM FAKULTESI DERGISI-PAMUKKALE UNIVERSITY JOURNAL OF EDUCATION | Article | J | Aksoy, BD; Cantürk Günhan, B; Mumcu, F | Aksoy, Behiye Dincer; Canturk Gunhan, Berna; Mumcu, Filiz | Mathematical thinking; Computational thinking; Mathematical modeling | ABSTRACTION | Mathematical thinking is critical to the maintenance of daily life and the development of science. This study examines the epistemological connection between mathematical and Computational Thinking (CT). Since CT involves the problem-solving process through thinking and computer science tools, it is thought to have an important relationship with mathematical thinking. In order to understand this relationship, mathematical modeling problems are addressed from a cognitive perspective in this study. Using a case study, a conceptual framework was developed by examining studies that attempt to integrate CT into mathematics education. In line with the framework, a professional development course was prepared and administered to a mathematics teacher. Subsequently, the relationship between the cognitive processes in the modeling process and the components of CT was examined. The study's findings revealed that (a) considering abstraction in the context of Piaget's abstraction theory is a more effective approach for understanding mathematical thinking, (b) more than one CT component can be identified at each stage of the modeling process, and no single component can be exclusively associated with a single stage, and (c) common and distinct thinking processes between mathematical thinking and CT were uncovered. These findings contribute to a more nuanced comprehension of the intricate interrelationships between mathematical thinking and cognitive flexibility. | 1.0 | 1.0 | 67.0 | Education & Educational Research | Education & Educational Research | DERGIPARK AKAD | English | Dokuz Eylul University; Dokuz Eylul University; Celal Bayar University | [Aksoy, Behiye Dincer] Dokuz Eylul Univ, Egitim Bilimleri Enstitusu, Izmir, Turkiye; [Canturk Gunhan, Berna] Dokuz Eylul Univ, Izmir, Turkiye; [Mumcu, Filiz] Celal Bayar Univ, Yunusemre, Turkiye | True |
| 10828 | 10.9779/pauefd.696511 | The Relationship between Mathematics Teachers' Mind Types and Computational Thinking Skills | 2021 | PAMUKKALE UNIVERSITESI EGITIM FAKULTESI DERGISI-PAMUKKALE UNIVERSITY JOURNAL OF EDUCATION | Article | 3.0 | False | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | The Relationship between Mathematics Teachers' Mind Types and Computational Thinking Skills | 2021.0 | PAMUKKALE UNIVERSITESI EGITIM FAKULTESI DERGISI-PAMUKKALE UNIVERSITY JOURNAL OF EDUCATION | Article | J | Hidiroglu, YÖ; Hidiroglu, ÇN | Hidiroglu, Yeliz Ozkan; Hidiroglu, Caglar Naci | Computational thinking; the five minds for future; mathematics teacher | FRAMEWORK; VALIDITY | The aim of the study is to investigate the relationship between the mind types of mathematics teachers which will shape the future and their computational thinking skills. The study was designed according to quantitative-relational survey model. This study was carried out with 481 volunteer mathematics teachers determined according to the random sampling method. Computational Thinking Skills Scale and Mind Types Scale were used as data collection tools in the study. In the analysis of the data, descriptive statistics, correlation and regression analyses were benefited. According to the perceptions of the mathematics teachers, the level of their ethical mind and computational thinking skills are very high while their disciplined mind, synthesizing mind, creating mind, respectful mind and quinary mind levels are high. Also, according to the perceptions of mathematics teachers, there is a high level significant positive relationship between their quinary minds and computational thinking skills, and their quinary minds (both in sub-dimesnions and as a whole) are a significant predictor of their computational thinking. | 1.0 | 3.0 | 46.0 | Education & Educational Research | Education & Educational Research | DERGIPARK AKAD | English | Ministry of National Education - Turkey; Pamukkale University | [Hidiroglu, Yeliz Ozkan] Republ Turkey Minist Natl Educ, Ankara, Turkey; [Hidiroglu, Caglar Naci] Pamukkale Univ, Math Educ, Denizli, Turkey | True |
| 10829 | 10.9781/ijimai.2021.03.001 | Foundations for the design of a creative system based on the analysis of the main techniques that stimulate human creativity | 2021 | International Journal of Interactive Multimedia and Artificial Intelligence | Article | 2.0 | True | False | de Garrido, L.; Gómez-Sanz, J.J.; Pavón, J. | Artificial Intelligence Creative System; Creativity; Lateral Thinking; Methods to Stimulate Human Creativity; Multi-agent System | NaN | This work presents the design of a computational system with creative capacity, based on the synthesis of the main methods that stimulate human creativity. When analyzing each method, a set of characteristics that the computer system must have in order to emulate a creative capacity has been suggested. In this way, by integrating all the suggestions in a structured way, it is possible to design the general architecture and functioning strategy of a computer system that has the incremental creative capacity of well-known creative methods. This computational system is designed as a multi-agent system, made up of two groups of agents, the problem solving group and the creative group, the first one exploring and evaluating paths for suitable solutions, the second implementing creative methods to generate new paths that are provided to the first group. © 2021, Universidad Internacional de la Rioja. All rights reserved. | 2.0 | de Garrido, Luis, Agent-based modeling of collaborative creative processes with INGENIAS, AI Communications, 32, 3, pp. 223-233, (2019); Boden, Margaret A., Creativity and artificial intelligence, Artificial Intelligence, 103, 1-2, pp. 347-356, (1998); Artificial Intelligence, (1996); Jennings, Kyle E., Developing creativity: Artificial barriers in artificial intelligence, Minds and Machines, 20, 4, pp. 489-501, (2010); Intelligence Without Reason, (1991); Phi Delta Kappan, (1961); Jordanous, Anna, Four PPPPerspectives on computational creativity in theory and in practice, Connection Science, 28, 2, pp. 194-216, (2016); Lamb, Carolyn Elizabeth, Evaluating computational creativity: An interdisciplinary tutorial, ACM Computing Surveys, 51, 2, (2019); Wiggins, Geraint A., A preliminary framework for description, analysis and comparison of creative systems, Knowledge-Based Systems, 19, 7, pp. 449-458, (2006); Role of Creativity in the Management of Innovation, (2017) | Universidad Internacional de la Rioja | English | Department of Architecture, Universitat de València, Valencia, Valencia, Spain; Institute of Knowledge Technology, Universidad Complutense de Madrid, Madrid, Madrid, Spain | NaN | True | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 10830 | NaN | Cultivating Computational Thinking Skills via Educational Robotics Activities in a Blended Learning Environment | 2025 | CEUR Workshop Proceedings | Conference paper | 0.0 | True | True | Pappa, N. | Blended Learning; Computational Thinking; Educational Robotics; Robotics Simulators; Secondary Education1 | Adversarial machine learning; Educational robots; Federated learning; Robot learning; Blended learning; Blended learning environments; Computational thinkings; Educational levels; Educational robotics; Learning context; Learning outcome; Robotic simulator; Secondary education1; Thinking skills; Contrastive Learning | There is a significant trend in the integration of Educational Robotics at all educational levels, and along with this, the promotion of Computational Thinking is one of the related learning outcomes of this integration. A t the s ame t ime, the t ransfer o f f ace-to-face learning to online or Blended Learning context due to the COVID-19 pandemic has led to the development of several technological tools, such as Educational Robotic simulators and online collaborating environments, to support this transfer. In this field, this PhD research aims to design a framework in which students collaborate in a Blended Learning context while solving Educational Robotic activities to cultivate Computational Thinking skills. © 2025 Copyright for this paper by its authors. | 0.0 | Wing, Jeannette M., Computational thinking, Communications of the ACM, 49, 3, pp. 33-35, (2006); Developing Computational Thinking in Compulsory Education, (2016); Atmatzidou, Soumela, How Does the Degree of Guidance Support Students’ Metacognitive and Problem Solving Skills in Educational Robotics?, Journal of Science Education and Technology, 27, 1, pp. 70-85, (2018); Chiazzese, Giuseppe, Educational robotics in primary school: Measuring the development of computational thinking skills with the bebras tasks, Informatics, 6, 4, (2019); Chevalier, Morgane, Fostering computational thinking through educational robotics: a model for creative computational problem solving, International Journal of STEM Education, 7, 1, (2020); Ioannou, Andri, Exploring the potentials of educational robotics in the development of computational thinking: A summary of current research and practical proposal for future work, Education and Information Technologies, 23, 6, pp. 2531-2544, (2018); Alves Gomes, Andresa Shirley, Educational Robotics in Times of Pandemic: Challenges and Possibilities, (2020); Proceedings of the 2012 Annual Meeting of the American Educational Research Association, (2012); Román-González, Marcos, Combining Assessment Tools for a Comprehensive Evaluation of Computational Thinking Interventions, pp. 79-98, (2019); Chevalier, Morgane, The role of feedback and guidance as intervention methods to foster computational thinking in educational robotics learning activities for primary school, Computers and Education, 180, (2022) | CEUR-WS | English | University of West Attica, Athens, Attica, Greece | NaN | True | Exploring the Differences in the Cultivation of Computational Thinking in Primary through Meta-analysis based on the Perspective of the Contrast between the East and the West | 2021.0 | 29TH INTERNATIONAL CONFERENCE ON COMPUTERS IN EDUCATION (ICCE 2021), VOL II | Proceedings Paper | C | Guan, X; Wei, GX; Jiang, B; Feng, X | Guan, Xiu; Wei, Guoxia; Jiang, Bo; Feng, Xiang | Computational thinking; comparative analysis; meta-analysis | LEARNING-PERFORMANCE; GAME | With the advent of the era of intelligent education, the cultivation and development of computational thinking is the key in talent training. However, most of the existing researches focus on the design of computational thinking teaching methods and models on a small scale, and lack the test of the training effect. Moreover, these effects in existing research are also mixed and fuzzed, and there are even greater differences between the East and the West. Therefore, in order to be able to analyze the effects of computational thinking teaching in depth, meta-analysis can be used to extract the factors that influence the effects of computational thinking in the related research on computational thinking training in the primary school stage in the East and the West. Through the calculation of experimental effect size, the effects of different studies are merged, so as to present the true effect of computational thinking training. A total of 30 qualified literatures were filtered, and 278 effect values were extracted from them. Based on these, the difference in training effects between the East and the West can be calculated to further analyze the development differences of computational thinking in different regions and teaching methods, and then point out the direction for the improvement of computational thinking training methods and models between different regions. The main value of the research is promoting the innovative development of computational thinking training within the globe. | 1.0 | 1.0 | 34.0 | Computer Science, Interdisciplinary Applications; Education & Educational Research | Computer Science; Education & Educational Research | ASIA PACIFIC SOC COMPUTERS IN EDUCATION | English | East China Normal University; Zhejiang University of Technology | [Guan, Xiu; Jiang, Bo; Feng, Xiang] East China Normal Univ, Dept Educ Informat Technol, Shanghai, Peoples R China; [Wei, Guoxia] Zhejiang Univ Technol, Coll Educ, Hangzhou, Zhejiang, Peoples R China | True |